EXPLORATORY DATA ANALYSIS
Reading Bigger Data Files using read.csv() Command.
d2 = read.csv(file.choose())
d2Getting information of an Object using summary() command.
summary(d2)## Name Total HP Attack
## Length:1045 Min. : 175.0 Min. : 1.00 Min. : 5.00
## Class :character 1st Qu.: 330.0 1st Qu.: 50.00 1st Qu.: 55.00
## Mode :character Median : 458.0 Median : 68.00 Median : 77.00
## Mean : 439.3 Mean : 70.07 Mean : 80.47
## 3rd Qu.: 515.0 3rd Qu.: 82.00 3rd Qu.:100.00
## Max. :1125.0 Max. :255.00 Max. :190.00
## Defence Sp_attack Sp_defence Speed
## Min. : 5.00 Min. : 10.00 Min. : 20.00 Min. : 5.00
## 1st Qu.: 50.00 1st Qu.: 50.00 1st Qu.: 50.00 1st Qu.: 45.00
## Median : 70.00 Median : 65.00 Median : 70.00 Median : 65.00
## Mean : 74.66 Mean : 73.02 Mean : 72.29 Mean : 68.81
## 3rd Qu.: 90.00 3rd Qu.: 95.00 3rd Qu.: 90.00 3rd Qu.: 90.00
## Max. :250.00 Max. :194.00 Max. :250.00 Max. :200.00Use of names() and dimnames() command to view the column and row elements of an Object.
rownames(d2)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
## [11] "11" "12" "13" "14" "15" "16" "17" "18" "19" "20"
## [21] "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
## [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40"
## [41] "41" "42" "43" "44" "45" "46" "47" "48" "49" "50"
## [51] "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70"
## [71] "71" "72" "73" "74" "75" "76" "77" "78" "79" "80"
## [81] "81" "82" "83" "84" "85" "86" "87" "88" "89" "90"
## [91] "91" "92" "93" "94" "95" "96" "97" "98" "99" "100"
## [101] "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
## [111] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130"
## [131] "131" "132" "133" "134" "135" "136" "137" "138" "139" "140"
## [141] "141" "142" "143" "144" "145" "146" "147" "148" "149" "150"
## [151] "151" "152" "153" "154" "155" "156" "157" "158" "159" "160"
## [161] "161" "162" "163" "164" "165" "166" "167" "168" "169" "170"
## [171] "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190"
## [191] "191" "192" "193" "194" "195" "196" "197" "198" "199" "200"
## [201] "201" "202" "203" "204" "205" "206" "207" "208" "209" "210"
## [211] "211" "212" "213" "214" "215" "216" "217" "218" "219" "220"
## [221] "221" "222" "223" "224" "225" "226" "227" "228" "229" "230"
## [231] "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
## [241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250"
## [251] "251" "252" "253" "254" "255" "256" "257" "258" "259" "260"
## [261] "261" "262" "263" "264" "265" "266" "267" "268" "269" "270"
## [271] "271" "272" "273" "274" "275" "276" "277" "278" "279" "280"
## [281] "281" "282" "283" "284" "285" "286" "287" "288" "289" "290"
## [291] "291" "292" "293" "294" "295" "296" "297" "298" "299" "300"
## [301] "301" "302" "303" "304" "305" "306" "307" "308" "309" "310"
## [311] "311" "312" "313" "314" "315" "316" "317" "318" "319" "320"
## [321] "321" "322" "323" "324" "325" "326" "327" "328" "329" "330"
## [331] "331" "332" "333" "334" "335" "336" "337" "338" "339" "340"
## [341] "341" "342" "343" "344" "345" "346" "347" "348" "349" "350"
## [351] "351" "352" "353" "354" "355" "356" "357" "358" "359" "360"
## [361] "361" "362" "363" "364" "365" "366" "367" "368" "369" "370"
## [371] "371" "372" "373" "374" "375" "376" "377" "378" "379" "380"
## [381] "381" "382" "383" "384" "385" "386" "387" "388" "389" "390"
## [391] "391" "392" "393" "394" "395" "396" "397" "398" "399" "400"
## [401] "401" "402" "403" "404" "405" "406" "407" "408" "409" "410"
## [411] "411" "412" "413" "414" "415" "416" "417" "418" "419" "420"
## [421] "421" "422" "423" "424" "425" "426" "427" "428" "429" "430"
## [431] "431" "432" "433" "434" "435" "436" "437" "438" "439" "440"
## [441] "441" "442" "443" "444" "445" "446" "447" "448" "449" "450"
## [451] "451" "452" "453" "454" "455" "456" "457" "458" "459" "460"
## [461] "461" "462" "463" "464" "465" "466" "467" "468" "469" "470"
## [471] "471" "472" "473" "474" "475" "476" "477" "478" "479" "480"
## [481] "481" "482" "483" "484" "485" "486" "487" "488" "489" "490"
## [491] "491" "492" "493" "494" "495" "496" "497" "498" "499" "500"
## [501] "501" "502" "503" "504" "505" "506" "507" "508" "509" "510"
## [511] "511" "512" "513" "514" "515" "516" "517" "518" "519" "520"
## [521] "521" "522" "523" "524" "525" "526" "527" "528" "529" "530"
## [531] "531" "532" "533" "534" "535" "536" "537" "538" "539" "540"
## [541] "541" "542" "543" "544" "545" "546" "547" "548" "549" "550"
## [551] "551" "552" "553" "554" "555" "556" "557" "558" "559" "560"
## [561] "561" "562" "563" "564" "565" "566" "567" "568" "569" "570"
## [571] "571" "572" "573" "574" "575" "576" "577" "578" "579" "580"
## [581] "581" "582" "583" "584" "585" "586" "587" "588" "589" "590"
## [591] "591" "592" "593" "594" "595" "596" "597" "598" "599" "600"
## [601] "601" "602" "603" "604" "605" "606" "607" "608" "609" "610"
## [611] "611" "612" "613" "614" "615" "616" "617" "618" "619" "620"
## [621] "621" "622" "623" "624" "625" "626" "627" "628" "629" "630"
## [631] "631" "632" "633" "634" "635" "636" "637" "638" "639" "640"
## [641] "641" "642" "643" "644" "645" "646" "647" "648" "649" "650"
## [651] "651" "652" "653" "654" "655" "656" "657" "658" "659" "660"
## [661] "661" "662" "663" "664" "665" "666" "667" "668" "669" "670"
## [671] "671" "672" "673" "674" "675" "676" "677" "678" "679" "680"
## [681] "681" "682" "683" "684" "685" "686" "687" "688" "689" "690"
## [691] "691" "692" "693" "694" "695" "696" "697" "698" "699" "700"
## [701] "701" "702" "703" "704" "705" "706" "707" "708" "709" "710"
## [711] "711" "712" "713" "714" "715" "716" "717" "718" "719" "720"
## [721] "721" "722" "723" "724" "725" "726" "727" "728" "729" "730"
## [731] "731" "732" "733" "734" "735" "736" "737" "738" "739" "740"
## [741] "741" "742" "743" "744" "745" "746" "747" "748" "749" "750"
## [751] "751" "752" "753" "754" "755" "756" "757" "758" "759" "760"
## [761] "761" "762" "763" "764" "765" "766" "767" "768" "769" "770"
## [771] "771" "772" "773" "774" "775" "776" "777" "778" "779" "780"
## [781] "781" "782" "783" "784" "785" "786" "787" "788" "789" "790"
## [791] "791" "792" "793" "794" "795" "796" "797" "798" "799" "800"
## [801] "801" "802" "803" "804" "805" "806" "807" "808" "809" "810"
## [811] "811" "812" "813" "814" "815" "816" "817" "818" "819" "820"
## [821] "821" "822" "823" "824" "825" "826" "827" "828" "829" "830"
## [831] "831" "832" "833" "834" "835" "836" "837" "838" "839" "840"
## [841] "841" "842" "843" "844" "845" "846" "847" "848" "849" "850"
## [851] "851" "852" "853" "854" "855" "856" "857" "858" "859" "860"
## [861] "861" "862" "863" "864" "865" "866" "867" "868" "869" "870"
## [871] "871" "872" "873" "874" "875" "876" "877" "878" "879" "880"
## [881] "881" "882" "883" "884" "885" "886" "887" "888" "889" "890"
## [891] "891" "892" "893" "894" "895" "896" "897" "898" "899" "900"
## [901] "901" "902" "903" "904" "905" "906" "907" "908" "909" "910"
## [911] "911" "912" "913" "914" "915" "916" "917" "918" "919" "920"
## [921] "921" "922" "923" "924" "925" "926" "927" "928" "929" "930"
## [931] "931" "932" "933" "934" "935" "936" "937" "938" "939" "940"
## [941] "941" "942" "943" "944" "945" "946" "947" "948" "949" "950"
## [951] "951" "952" "953" "954" "955" "956" "957" "958" "959" "960"
## [961] "961" "962" "963" "964" "965" "966" "967" "968" "969" "970"
## [971] "971" "972" "973" "974" "975" "976" "977" "978" "979" "980"
## [981] "981" "982" "983" "984" "985" "986" "987" "988" "989" "990"
## [991] "991" "992" "993" "994" "995" "996" "997" "998" "999" "1000"
## [1001] "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008" "1009" "1010"
## [1011] "1011" "1012" "1013" "1014" "1015" "1016" "1017" "1018" "1019" "1020"
## [1021] "1021" "1022" "1023" "1024" "1025" "1026" "1027" "1028" "1029" "1030"
## [1031] "1031" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1040"
## [1041] "1041" "1042" "1043" "1044" "1045"colnames(d2)## [1] "Name" "Total" "HP" "Attack" "Defence"
## [6] "Sp_attack" "Sp_defence" "Speed"plot(d2)
Summarizing samples:
list(d2)## [[1]]
## Name Total HP Attack Defence Sp_attack Sp_defence Speed
## 1 Bulbasaur 318 45 49 49 65 65 45
## 2 Ivysaur 405 60 62 63 80 80 60
## 3 Venusaur 525 80 82 83 100 100 80
## 4 Mega Venusaur 625 80 100 123 122 120 80
## 5 Charmander 309 39 52 43 60 50 65
## 6 Charmeleon 405 58 64 58 80 65 80
## 7 Charizard 534 78 84 78 109 85 100
## 8 Mega Charizard 634 78 130 111 130 85 100
## 9 Mega Charizard X 634 78 104 78 159 115 100
## 10 Squirtle 314 44 48 65 50 64 43
## 11 Wartortle 405 59 63 80 65 80 58
## 12 Blastoise 530 79 83 100 85 105 78
## 13 Mega Blastoise 630 79 103 120 135 115 78
## 14 Caterpie 195 45 30 35 20 20 45
## 15 Metapod 205 50 20 55 25 25 30
## 16 Butterfree 395 60 45 50 90 80 70
## 17 Weedle 195 40 35 30 20 20 50
## 18 Kakuna 205 45 25 50 25 25 35
## 19 Beedrill 395 65 90 40 45 80 75
## 20 Mega Beedrill 495 65 150 40 15 80 145
## 21 Pidgey 251 40 45 40 35 35 56
## 22 Pidgeotto 349 63 60 55 50 50 71
## 23 Pidgeot 479 83 80 75 70 70 101
## 24 Mega Pidgeot 579 83 80 80 135 80 121
## 25 Rattata 253 30 56 35 25 35 72
## 26 Mega Rattata 253 30 56 35 25 35 72
## 27 Raticate 413 55 81 60 50 70 97
## 28 Mega Raticate 413 75 71 70 40 80 77
## 29 Spearow 262 40 60 30 31 31 70
## 30 Fearow 442 65 90 65 61 61 100
## 31 Ekans 288 35 60 44 40 54 55
## 32 Arbok 448 60 95 69 65 79 80
## 33 Pikachu 320 35 55 40 50 50 90
## 34 Mega Pikachu 430 45 80 50 75 60 120
## 35 Raichu 485 60 90 55 90 80 110
## 36 Mega Raichu 485 60 85 50 95 85 110
## 37 Sandshrew 300 50 75 85 20 30 40
## 38 Mega Sandshrew 300 50 75 90 10 35 40
## 39 Sandslash 450 75 100 110 45 55 65
## 40 Mega Sandslash 450 75 100 120 25 65 65
## 41 Nidoranâ\231\200 275 55 47 52 40 40 41
## 42 Nidorina 365 70 62 67 55 55 56
## 43 Nidoqueen 505 90 92 87 75 85 76
## 44 Nidoranâ\231‚ 273 46 57 40 40 40 50
## 45 Nidorino 365 61 72 57 55 55 65
## 46 Nidoking 505 81 102 77 85 75 85
## 47 Clefairy 323 70 45 48 60 65 35
## 48 Clefable 483 95 70 73 95 90 60
## 49 Vulpix 299 38 41 40 50 65 65
## 50 Mega Vulpix 299 38 41 40 50 65 65
## 51 Ninetales 505 73 76 75 81 100 100
## 52 Mega Ninetales 505 73 67 75 81 100 109
## 53 Jigglypuff 270 115 45 20 45 25 20
## 54 Wigglytuff 435 140 70 45 85 50 45
## 55 Zubat 245 40 45 35 30 40 55
## 56 Golbat 455 75 80 70 65 75 90
## 57 Oddish 320 45 50 55 75 65 30
## 58 Gloom 395 60 65 70 85 75 40
## 59 Vileplume 490 75 80 85 110 90 50
## 60 Paras 285 35 70 55 45 55 25
## 61 Parasect 405 60 95 80 60 80 30
## 62 Venonat 305 60 55 50 40 55 45
## 63 Venomoth 450 70 65 60 90 75 90
## 64 Diglett 265 10 55 25 35 45 95
## 65 Mega Diglett 265 10 55 30 35 45 90
## 66 Dugtrio 425 35 100 50 50 70 120
## 67 Mega Dugtrio 425 35 100 60 50 70 110
## 68 Meowth 290 40 45 35 40 40 90
## 69 Mega Meowth 290 40 35 35 50 40 90
## 70 Mega Meowth X 290 50 65 55 40 40 40
## 71 Persian 440 65 70 60 65 65 115
## 72 Mega Persian 440 65 60 60 75 65 115
## 73 Psyduck 320 50 52 48 65 50 55
## 74 Golduck 500 80 82 78 95 80 85
## 75 Mankey 305 40 80 35 35 45 70
## 76 Primeape 455 65 105 60 60 70 95
## 77 Growlithe 350 55 70 45 70 50 60
## 78 Arcanine 555 90 110 80 100 80 95
## 79 Poliwag 300 40 50 40 40 40 90
## 80 Poliwhirl 385 65 65 65 50 50 90
## 81 Poliwrath 510 90 95 95 70 90 70
## 82 Abra 310 25 20 15 105 55 90
## 83 Kadabra 400 40 35 30 120 70 105
## 84 Alakazam 500 55 50 45 135 95 120
## 85 Mega Alakazam 600 55 50 65 175 105 150
## 86 Machop 305 70 80 50 35 35 35
## 87 Machoke 405 80 100 70 50 60 45
## 88 Machamp 505 90 130 80 65 85 55
## 89 Bellsprout 300 50 75 35 70 30 40
## 90 Weepinbell 390 65 90 50 85 45 55
## 91 Victreebel 490 80 105 65 100 70 70
## 92 Tentacool 335 40 40 35 50 100 70
## 93 Tentacruel 515 80 70 65 80 120 100
## 94 Geodude 300 40 80 100 30 30 20
## 95 Mega Geodude 300 40 80 100 30 30 20
## 96 Graveler 390 55 95 115 45 45 35
## 97 Mega Graveler 390 55 95 115 45 45 35
## 98 Golem 495 80 120 130 55 65 45
## 99 Mega Golem 495 80 120 130 55 65 45
## 100 Ponyta 410 50 85 55 65 65 90
## 101 Mega Ponyta 410 50 85 55 65 65 90
## 102 Rapidash 500 65 100 70 80 80 105
## 103 Mega Rapidash 500 65 100 70 80 80 105
## 104 Slowpoke 315 90 65 65 40 40 15
## 105 Mega Slowpoke 315 90 65 65 40 40 15
## 106 Slowbro 490 95 75 110 100 80 30
## 107 Mega Slowbro 590 95 75 180 130 80 30
## 108 Mega Slowbro X 490 95 100 95 100 70 30
## 109 Magnemite 325 25 35 70 95 55 45
## 110 Magneton 465 50 60 95 120 70 70
## 111 Farfetch'd 377 52 90 55 58 62 60
## 112 Mega Farfetch'd 377 52 95 55 58 62 55
## 113 Doduo 310 35 85 45 35 35 75
## 114 Dodrio 470 60 110 70 60 60 110
## 115 Seel 325 65 45 55 45 70 45
## 116 Dewgong 475 90 70 80 70 95 70
## 117 Grimer 325 80 80 50 40 50 25
## 118 Mega Grimer 325 80 80 50 40 50 25
## 119 Muk 500 105 105 75 65 100 50
## 120 Mega Muk 500 105 105 75 65 100 50
## 121 Shellder 305 30 65 100 45 25 40
## 122 Cloyster 525 50 95 180 85 45 70
## 123 Gastly 310 30 35 30 100 35 80
## 124 Haunter 405 45 50 45 115 55 95
## 125 Gengar 500 60 65 60 130 75 110
## 126 Mega Gengar 600 60 65 80 170 95 130
## 127 Onix 385 35 45 160 30 45 70
## 128 Drowzee 328 60 48 45 43 90 42
## 129 Hypno 483 85 73 70 73 115 67
## 130 Krabby 325 30 105 90 25 25 50
## 131 Kingler 475 55 130 115 50 50 75
## 132 Voltorb 330 40 30 50 55 55 100
## 133 Electrode 490 60 50 70 80 80 150
## 134 Exeggcute 325 60 40 80 60 45 40
## 135 Exeggutor 530 95 95 85 125 75 55
## 136 Mega Exeggutor 530 95 105 85 125 75 45
## 137 Cubone 320 50 50 95 40 50 35
## 138 Marowak 425 60 80 110 50 80 45
## 139 Mega Marowak 425 60 80 110 50 80 45
## 140 Hitmonlee 455 50 120 53 35 110 87
## 141 Hitmonchan 455 50 105 79 35 110 76
## 142 Lickitung 385 90 55 75 60 75 30
## 143 Koffing 340 40 65 95 60 45 35
## 144 Weezing 490 65 90 120 85 70 60
## 145 Mega Weezing 490 65 90 120 85 70 60
## 146 Rhyhorn 345 80 85 95 30 30 25
## 147 Rhydon 485 105 130 120 45 45 40
## 148 Chansey 450 250 5 5 35 105 50
## 149 Tangela 435 65 55 115 100 40 60
## 150 Kangaskhan 490 105 95 80 40 80 90
## 151 Mega Kangaskhan 590 105 125 100 60 100 100
## 152 Horsea 295 30 40 70 70 25 60
## 153 Seadra 440 55 65 95 95 45 85
## 154 Goldeen 320 45 67 60 35 50 63
## 155 Seaking 450 80 92 65 65 80 68
## 156 Staryu 340 30 45 55 70 55 85
## 157 Starmie 520 60 75 85 100 85 115
## 158 Mr. Mime 460 40 45 65 100 120 90
## 159 Mega Mr. Mime 460 50 65 65 90 90 100
## 160 Scyther 500 70 110 80 55 80 105
## 161 Jynx 455 65 50 35 115 95 95
## 162 Electabuzz 490 65 83 57 95 85 105
## 163 Magmar 495 65 95 57 100 85 93
## 164 Pinsir 500 65 125 100 55 70 85
## 165 Mega Pinsir 600 65 155 120 65 90 105
## 166 Tauros 490 75 100 95 40 70 110
## 167 Magikarp 200 20 10 55 15 20 80
## 168 Gyarados 540 95 125 79 60 100 81
## 169 Mega Gyarados 640 95 155 109 70 130 81
## 170 Lapras 535 130 85 80 85 95 60
## 171 Ditto 288 48 48 48 48 48 48
## 172 Eevee 325 55 55 50 45 65 55
## 173 Mega Eevee 435 65 75 70 65 85 75
## 174 Vaporeon 525 130 65 60 110 95 65
## 175 Jolteon 525 65 65 60 110 95 130
## 176 Flareon 525 65 130 60 95 110 65
## 177 Porygon 395 65 60 70 85 75 40
## 178 Omanyte 355 35 40 100 90 55 35
## 179 Omastar 495 70 60 125 115 70 55
## 180 Kabuto 355 30 80 90 55 45 55
## 181 Kabutops 495 60 115 105 65 70 80
## 182 Aerodactyl 515 80 105 65 60 75 130
## 183 Mega Aerodactyl 615 80 135 85 70 95 150
## 184 Snorlax 540 160 110 65 65 110 30
## 185 Articuno 580 90 85 100 95 125 85
## 186 Mega Articuno 580 90 85 85 125 100 95
## 187 Zapdos 580 90 90 85 125 90 100
## 188 Mega Zapdos 580 90 125 90 85 90 100
## 189 Moltres 580 90 100 90 125 85 90
## 190 Mega Moltres 580 90 85 90 100 125 90
## 191 Dratini 300 41 64 45 50 50 50
## 192 Dragonair 420 61 84 65 70 70 70
## 193 Dragonite 600 91 134 95 100 100 80
## 194 Mewtwo 680 106 110 90 154 90 130
## 195 Mega Mewtwo 780 106 190 100 154 100 130
## 196 Mega Mewtwo X 780 106 150 70 194 120 140
## 197 Mew 600 100 100 100 100 100 100
## 198 Chikorita 318 45 49 65 49 65 45
## 199 Bayleef 405 60 62 80 63 80 60
## 200 Meganium 525 80 82 100 83 100 80
## 201 Cyndaquil 309 39 52 43 60 50 65
## 202 Quilava 405 58 64 58 80 65 80
## 203 Typhlosion 534 78 84 78 109 85 100
## 204 Totodile 314 50 65 64 44 48 43
## 205 Croconaw 405 65 80 80 59 63 58
## 206 Feraligatr 530 85 105 100 79 83 78
## 207 Sentret 215 35 46 34 35 45 20
## 208 Furret 415 85 76 64 45 55 90
## 209 Hoothoot 262 60 30 30 36 56 50
## 210 Noctowl 452 100 50 50 86 96 70
## 211 Ledyba 265 40 20 30 40 80 55
## 212 Ledian 390 55 35 50 55 110 85
## 213 Spinarak 250 40 60 40 40 40 30
## 214 Ariados 400 70 90 70 60 70 40
## 215 Crobat 535 85 90 80 70 80 130
## 216 Chinchou 330 75 38 38 56 56 67
## 217 Lanturn 460 125 58 58 76 76 67
## 218 Pichu 205 20 40 15 35 35 60
## 219 Cleffa 218 50 25 28 45 55 15
## 220 Igglybuff 210 90 30 15 40 20 15
## 221 Togepi 245 35 20 65 40 65 20
## 222 Togetic 405 55 40 85 80 105 40
## 223 Natu 320 40 50 45 70 45 70
## 224 Xatu 470 65 75 70 95 70 95
## 225 Mareep 280 55 40 40 65 45 35
## 226 Flaaffy 365 70 55 55 80 60 45
## 227 Ampharos 510 90 75 85 115 90 55
## 228 Mega Ampharos 610 90 95 105 165 110 45
## 229 Bellossom 490 75 80 95 90 100 50
## 230 Marill 250 70 20 50 20 50 40
## 231 Azumarill 420 100 50 80 60 80 50
## 232 Sudowoodo 410 70 100 115 30 65 30
## 233 Politoed 500 90 75 75 90 100 70
## 234 Hoppip 250 35 35 40 35 55 50
## 235 Skiploom 340 55 45 50 45 65 80
## 236 Jumpluff 460 75 55 70 55 95 110
## 237 Aipom 360 55 70 55 40 55 85
## 238 Sunkern 180 30 30 30 30 30 30
## 239 Sunflora 425 75 75 55 105 85 30
## 240 Yanma 390 65 65 45 75 45 95
## 241 Wooper 210 55 45 45 25 25 15
## 242 Quagsire 430 95 85 85 65 65 35
## 243 Espeon 525 65 65 60 130 95 110
## 244 Umbreon 525 95 65 110 60 130 65
## 245 Murkrow 405 60 85 42 85 42 91
## 246 Slowking 490 95 75 80 100 110 30
## 247 Mega Slowking 490 95 65 80 110 110 30
## 248 Misdreavus 435 60 60 60 85 85 85
## 249 Unown 336 48 72 48 72 48 48
## 250 Wobbuffet 405 190 33 58 33 58 33
## 251 Girafarig 455 70 80 65 90 65 85
## 252 Pineco 290 50 65 90 35 35 15
## 253 Forretress 465 75 90 140 60 60 40
## 254 Dunsparce 415 100 70 70 65 65 45
## 255 Gligar 430 65 75 105 35 65 85
## 256 Steelix 510 75 85 200 55 65 30
## 257 Mega Steelix 610 75 125 230 55 95 30
## 258 Snubbull 300 60 80 50 40 40 30
## 259 Granbull 450 90 120 75 60 60 45
## 260 Qwilfish 440 65 95 85 55 55 85
## 261 Scizor 500 70 130 100 55 80 65
## 262 Mega Scizor 600 70 150 140 65 100 75
## 263 Shuckle 505 20 10 230 10 230 5
## 264 Heracross 500 80 125 75 40 95 85
## 265 Mega Heracross 600 80 185 115 40 105 75
## 266 Sneasel 430 55 95 55 35 75 115
## 267 Teddiursa 330 60 80 50 50 50 40
## 268 Ursaring 500 90 130 75 75 75 55
## 269 Slugma 250 40 40 40 70 40 20
## 270 Magcargo 430 60 50 120 90 80 30
## 271 Swinub 250 50 50 40 30 30 50
## 272 Piloswine 450 100 100 80 60 60 50
## 273 Corsola 410 65 55 95 65 95 35
## 274 Mega Corsola 410 60 55 100 65 100 30
## 275 Remoraid 300 35 65 35 65 35 65
## 276 Octillery 480 75 105 75 105 75 45
## 277 Delibird 330 45 55 45 65 45 75
## 278 Mantine 485 85 40 70 80 140 70
## 279 Skarmory 465 65 80 140 40 70 70
## 280 Houndour 330 45 60 30 80 50 65
## 281 Houndoom 500 75 90 50 110 80 95
## 282 Mega Houndoom 600 75 90 90 140 90 115
## 283 Kingdra 540 75 95 95 95 95 85
## 284 Phanpy 330 90 60 60 40 40 40
## 285 Donphan 500 90 120 120 60 60 50
## 286 Porygon2 515 85 80 90 105 95 60
## 287 Stantler 465 73 95 62 85 65 85
## 288 Smeargle 250 55 20 35 20 45 75
## 289 Tyrogue 210 35 35 35 35 35 35
## 290 Hitmontop 455 50 95 95 35 110 70
## 291 Smoochum 305 45 30 15 85 65 65
## 292 Elekid 360 45 63 37 65 55 95
## 293 Magby 365 45 75 37 70 55 83
## 294 Miltank 490 95 80 105 40 70 100
## 295 Blissey 540 255 10 10 75 135 55
## 296 Raikou 580 90 85 75 115 100 115
## 297 Entei 580 115 115 85 90 75 100
## 298 Suicune 580 100 75 115 90 115 85
## 299 Larvitar 300 50 64 50 45 50 41
## 300 Pupitar 410 70 84 70 65 70 51
## 301 Tyranitar 600 100 134 110 95 100 61
## 302 Mega Tyranitar 700 100 164 150 95 120 71
## 303 Lugia 680 106 90 130 90 154 110
## 304 Ho-oh 680 106 130 90 110 154 90
## 305 Celebi 600 100 100 100 100 100 100
## 306 Treecko 310 40 45 35 65 55 70
## 307 Grovyle 405 50 65 45 85 65 95
## 308 Sceptile 530 70 85 65 105 85 120
## 309 Mega Sceptile 630 70 110 75 145 85 145
## 310 Torchic 310 45 60 40 70 50 45
## 311 Combusken 405 60 85 60 85 60 55
## 312 Blaziken 530 80 120 70 110 70 80
## 313 Mega Blaziken 630 80 160 80 130 80 100
## 314 Mudkip 310 50 70 50 50 50 40
## 315 Marshtomp 405 70 85 70 60 70 50
## 316 Swampert 535 100 110 90 85 90 60
## 317 Mega Swampert 635 100 150 110 95 110 70
## 318 Poochyena 220 35 55 35 30 30 35
## 319 Mightyena 420 70 90 70 60 60 70
## 320 Zigzagoon 240 38 30 41 30 41 60
## 321 Mega Zigzagoon 240 38 30 41 30 41 60
## 322 Linoone 420 78 70 61 50 61 100
## 323 Mega Linoone 420 78 70 61 50 61 100
## 324 Wurmple 195 45 45 35 20 30 20
## 325 Silcoon 205 50 35 55 25 25 15
## 326 Beautifly 395 60 70 50 100 50 65
## 327 Cascoon 205 50 35 55 25 25 15
## 328 Dustox 385 60 50 70 50 90 65
## 329 Lotad 220 40 30 30 40 50 30
## 330 Lombre 340 60 50 50 60 70 50
## 331 Ludicolo 480 80 70 70 90 100 70
## 332 Seedot 220 40 40 50 30 30 30
## 333 Nuzleaf 340 70 70 40 60 40 60
## 334 Shiftry 480 90 100 60 90 60 80
## 335 Taillow 270 40 55 30 30 30 85
## 336 Swellow 455 60 85 60 75 50 125
## 337 Wingull 270 40 30 30 55 30 85
## 338 Pelipper 440 60 50 100 95 70 65
## 339 Ralts 198 28 25 25 45 35 40
## 340 Kirlia 278 38 35 35 65 55 50
## 341 Gardevoir 518 68 65 65 125 115 80
## 342 Mega Gardevoir 618 68 85 65 165 135 100
## 343 Surskit 269 40 30 32 50 52 65
## 344 Masquerain 454 70 60 62 100 82 80
## 345 Shroomish 295 60 40 60 40 60 35
## 346 Breloom 460 60 130 80 60 60 70
## 347 Slakoth 280 60 60 60 35 35 30
## 348 Vigoroth 440 80 80 80 55 55 90
## 349 Slaking 670 150 160 100 95 65 100
## 350 Nincada 266 31 45 90 30 30 40
## 351 Ninjask 456 61 90 45 50 50 160
## 352 Shedinja 236 1 90 45 30 30 40
## 353 Whismur 240 64 51 23 51 23 28
## 354 Loudred 360 84 71 43 71 43 48
## 355 Exploud 490 104 91 63 91 73 68
## 356 Makuhita 237 72 60 30 20 30 25
## 357 Hariyama 474 144 120 60 40 60 50
## 358 Azurill 190 50 20 40 20 40 20
## 359 Nosepass 375 30 45 135 45 90 30
## 360 Skitty 260 50 45 45 35 35 50
## 361 Delcatty 400 70 65 65 55 55 90
## 362 Sableye 380 50 75 75 65 65 50
## 363 Mega Sableye 480 50 85 125 85 115 20
## 364 Mawile 380 50 85 85 55 55 50
## 365 Mega Mawile 480 50 105 125 55 95 50
## 366 Aron 330 50 70 100 40 40 30
## 367 Lairon 430 60 90 140 50 50 40
## 368 Aggron 530 70 110 180 60 60 50
## 369 Mega Aggron 630 70 140 230 60 80 50
## 370 Meditite 280 30 40 55 40 55 60
## 371 Medicham 410 60 60 75 60 75 80
## 372 Mega Medicham 510 60 100 85 80 85 100
## 373 Electrike 295 40 45 40 65 40 65
## 374 Manectric 475 70 75 60 105 60 105
## 375 Mega Manectric 575 70 75 80 135 80 135
## 376 Plusle 405 60 50 40 85 75 95
## 377 Minun 405 60 40 50 75 85 95
## 378 Volbeat 430 65 73 75 47 85 85
## 379 Illumise 430 65 47 75 73 85 85
## 380 Roselia 400 50 60 45 100 80 65
## 381 Gulpin 302 70 43 53 43 53 40
## 382 Swalot 467 100 73 83 73 83 55
## 383 Carvanha 305 45 90 20 65 20 65
## 384 Sharpedo 460 70 120 40 95 40 95
## 385 Mega Sharpedo 560 70 140 70 110 65 105
## 386 Wailmer 400 130 70 35 70 35 60
## 387 Wailord 500 170 90 45 90 45 60
## 388 Numel 305 60 60 40 65 45 35
## 389 Camerupt 460 70 100 70 105 75 40
## 390 Mega Camerupt 560 70 120 100 145 105 20
## 391 Torkoal 470 70 85 140 85 70 20
## 392 Spoink 330 60 25 35 70 80 60
## 393 Grumpig 470 80 45 65 90 110 80
## 394 Spinda 360 60 60 60 60 60 60
## 395 Trapinch 290 45 100 45 45 45 10
## 396 Vibrava 340 50 70 50 50 50 70
## 397 Flygon 520 80 100 80 80 80 100
## 398 Cacnea 335 50 85 40 85 40 35
## 399 Cacturne 475 70 115 60 115 60 55
## 400 Swablu 310 45 40 60 40 75 50
## 401 Altaria 490 75 70 90 70 105 80
## 402 Mega Altaria 590 75 110 110 110 105 80
## 403 Zangoose 458 73 115 60 60 60 90
## 404 Seviper 458 73 100 60 100 60 65
## 405 Lunatone 460 90 55 65 95 85 70
## 406 Solrock 460 90 95 85 55 65 70
## 407 Barboach 288 50 48 43 46 41 60
## 408 Whiscash 468 110 78 73 76 71 60
## 409 Corphish 308 43 80 65 50 35 35
## 410 Crawdaunt 468 63 120 85 90 55 55
## 411 Baltoy 300 40 40 55 40 70 55
## 412 Claydol 500 60 70 105 70 120 75
## 413 Lileep 355 66 41 77 61 87 23
## 414 Cradily 495 86 81 97 81 107 43
## 415 Anorith 355 45 95 50 40 50 75
## 416 Armaldo 495 75 125 100 70 80 45
## 417 Feebas 200 20 15 20 10 55 80
## 418 Milotic 540 95 60 79 100 125 81
## 419 Castform 420 70 70 70 70 70 70
## 420 Mega Castform 420 70 70 70 70 70 70
## 421 Mega Castform X 420 70 70 70 70 70 70
## 422 Mega Castform X 420 70 70 70 70 70 70
## 423 Kecleon 440 60 90 70 60 120 40
## 424 Shuppet 295 44 75 35 63 33 45
## 425 Banette 455 64 115 65 83 63 65
## 426 Mega Banette 555 64 165 75 93 83 75
## 427 Duskull 295 20 40 90 30 90 25
## 428 Dusclops 455 40 70 130 60 130 25
## 429 Tropius 460 99 68 83 72 87 51
## 430 Chimecho 455 75 50 80 95 90 65
## 431 Absol 465 65 130 60 75 60 75
## 432 Mega Absol 565 65 150 60 115 60 115
## 433 Wynaut 260 95 23 48 23 48 23
## 434 Snorunt 300 50 50 50 50 50 50
## 435 Glalie 480 80 80 80 80 80 80
## 436 Mega Glalie 580 80 120 80 120 80 100
## 437 Spheal 290 70 40 50 55 50 25
## 438 Sealeo 410 90 60 70 75 70 45
## 439 Walrein 530 110 80 90 95 90 65
## 440 Clamperl 345 35 64 85 74 55 32
## 441 Huntail 485 55 104 105 94 75 52
## 442 Gorebyss 485 55 84 105 114 75 52
## 443 Relicanth 485 100 90 130 45 65 55
## 444 Luvdisc 330 43 30 55 40 65 97
## 445 Bagon 300 45 75 60 40 30 50
## 446 Shelgon 420 65 95 100 60 50 50
## 447 Salamence 600 95 135 80 110 80 100
## 448 Mega Salamence 700 95 145 130 120 90 120
## 449 Beldum 300 40 55 80 35 60 30
## 450 Metang 420 60 75 100 55 80 50
## 451 Metagross 600 80 135 130 95 90 70
## 452 Mega Metagross 700 80 145 150 105 110 110
## 453 Regirock 580 80 100 200 50 100 50
## 454 Regice 580 80 50 100 100 200 50
## 455 Registeel 580 80 75 150 75 150 50
## 456 Latias 600 80 80 90 110 130 110
## 457 Mega Latias 700 80 100 120 140 150 110
## 458 Latios 600 80 90 80 130 110 110
## 459 Mega Latios 700 80 130 100 160 120 110
## 460 Kyogre 670 100 100 90 150 140 90
## 461 Mega Kyogre 770 100 150 90 180 160 90
## 462 Groudon 670 100 150 140 100 90 90
## 463 Mega Groudon 770 100 180 160 150 90 90
## 464 Rayquaza 680 105 150 90 150 90 95
## 465 Mega Rayquaza 780 105 180 100 180 100 115
## 466 Jirachi 600 100 100 100 100 100 100
## 467 Deoxys 600 50 150 50 150 50 150
## 468 Mega Deoxys 600 50 180 20 180 20 150
## 469 Mega Deoxys X 600 50 70 160 70 160 90
## 470 Mega Deoxys X 600 50 95 90 95 90 180
## 471 Turtwig 318 55 68 64 45 55 31
## 472 Grotle 405 75 89 85 55 65 36
## 473 Torterra 525 95 109 105 75 85 56
## 474 Chimchar 309 44 58 44 58 44 61
## 475 Monferno 405 64 78 52 78 52 81
## 476 Infernape 534 76 104 71 104 71 108
## 477 Piplup 314 53 51 53 61 56 40
## 478 Prinplup 405 64 66 68 81 76 50
## 479 Empoleon 530 84 86 88 111 101 60
## 480 Starly 245 40 55 30 30 30 60
## 481 Staravia 340 55 75 50 40 40 80
## 482 Staraptor 485 85 120 70 50 60 100
## 483 Bidoof 250 59 45 40 35 40 31
## 484 Bibarel 410 79 85 60 55 60 71
## 485 Kricketot 194 37 25 41 25 41 25
## 486 Kricketune 384 77 85 51 55 51 65
## 487 Shinx 263 45 65 34 40 34 45
## 488 Luxio 363 60 85 49 60 49 60
## 489 Luxray 523 80 120 79 95 79 70
## 490 Budew 280 40 30 35 50 70 55
## 491 Roserade 515 60 70 65 125 105 90
## 492 Cranidos 350 67 125 40 30 30 58
## 493 Rampardos 495 97 165 60 65 50 58
## 494 Shieldon 350 30 42 118 42 88 30
## 495 Bastiodon 495 60 52 168 47 138 30
## 496 Burmy 224 40 29 45 29 45 36
## 497 Wormadam 424 60 59 85 79 105 36
## 498 Mega Wormadam 424 60 79 105 59 85 36
## 499 Mega Wormadam X 424 60 69 95 69 95 36
## 500 Mothim 424 70 94 50 94 50 66
## 501 Combee 244 30 30 42 30 42 70
## 502 Vespiquen 474 70 80 102 80 102 40
## 503 Pachirisu 405 60 45 70 45 90 95
## 504 Buizel 330 55 65 35 60 30 85
## 505 Floatzel 495 85 105 55 85 50 115
## 506 Cherubi 275 45 35 45 62 53 35
## 507 Cherrim 450 70 60 70 87 78 85
## 508 Shellos 325 76 48 48 57 62 34
## 509 Gastrodon 475 111 83 68 92 82 39
## 510 Ambipom 482 75 100 66 60 66 115
## 511 Drifloon 348 90 50 34 60 44 70
## 512 Drifblim 498 150 80 44 90 54 80
## 513 Buneary 350 55 66 44 44 56 85
## 514 Lopunny 480 65 76 84 54 96 105
## 515 Mega Lopunny 580 65 136 94 54 96 135
## 516 Mismagius 495 60 60 60 105 105 105
## 517 Honchkrow 505 100 125 52 105 52 71
## 518 Glameow 310 49 55 42 42 37 85
## 519 Purugly 452 71 82 64 64 59 112
## 520 Chingling 285 45 30 50 65 50 45
## 521 Stunky 329 63 63 47 41 41 74
## 522 Skuntank 479 103 93 67 71 61 84
## 523 Bronzor 300 57 24 86 24 86 23
## 524 Bronzong 500 67 89 116 79 116 33
## 525 Bonsly 290 50 80 95 10 45 10
## 526 Mime Jr. 310 20 25 45 70 90 60
## 527 Happiny 220 100 5 5 15 65 30
## 528 Chatot 411 76 65 45 92 42 91
## 529 Spiritomb 485 50 92 108 92 108 35
## 530 Gible 300 58 70 45 40 45 42
## 531 Gabite 410 68 90 65 50 55 82
## 532 Garchomp 600 108 130 95 80 85 102
## 533 Mega Garchomp 700 108 170 115 120 95 92
## 534 Munchlax 390 135 85 40 40 85 5
## 535 Riolu 285 40 70 40 35 40 60
## 536 Lucario 525 70 110 70 115 70 90
## 537 Mega Lucario 625 70 145 88 140 70 112
## 538 Hippopotas 330 68 72 78 38 42 32
## 539 Hippowdon 525 108 112 118 68 72 47
## 540 Skorupi 330 40 50 90 30 55 65
## 541 Drapion 500 70 90 110 60 75 95
## 542 Croagunk 300 48 61 40 61 40 50
## 543 Toxicroak 490 83 106 65 86 65 85
## 544 Carnivine 454 74 100 72 90 72 46
## 545 Finneon 330 49 49 56 49 61 66
## 546 Lumineon 460 69 69 76 69 86 91
## 547 Mantyke 345 45 20 50 60 120 50
## 548 Snover 334 60 62 50 62 60 40
## 549 Abomasnow 494 90 92 75 92 85 60
## 550 Mega Abomasnow 594 90 132 105 132 105 30
## 551 Weavile 510 70 120 65 45 85 125
## 552 Magnezone 535 70 70 115 130 90 60
## 553 Lickilicky 515 110 85 95 80 95 50
## 554 Rhyperior 535 115 140 130 55 55 40
## 555 Tangrowth 535 100 100 125 110 50 50
## 556 Electivire 540 75 123 67 95 85 95
## 557 Magmortar 540 75 95 67 125 95 83
## 558 Togekiss 545 85 50 95 120 115 80
## 559 Yanmega 515 86 76 86 116 56 95
## 560 Leafeon 525 65 110 130 60 65 95
## 561 Glaceon 525 65 60 110 130 95 65
## 562 Gliscor 510 75 95 125 45 75 95
## 563 Mamoswine 530 110 130 80 70 60 80
## 564 Porygon-Z 535 85 80 70 135 75 90
## 565 Gallade 518 68 125 65 65 115 80
## 566 Mega Gallade 618 68 165 95 65 115 110
## 567 Probopass 525 60 55 145 75 150 40
## 568 Dusknoir 525 45 100 135 65 135 45
## 569 Froslass 480 70 80 70 80 70 110
## 570 Rotom 440 50 50 77 95 77 91
## 571 Mega Rotom 520 50 65 107 105 107 86
## 572 Mega Rotom X 520 50 65 107 105 107 86
## 573 Mega Rotom X 520 50 65 107 105 107 86
## 574 Mega Rotom X 520 50 65 107 105 107 86
## 575 Mega Rotom X 520 50 65 107 105 107 86
## 576 Uxie 580 75 75 130 75 130 95
## 577 Mesprit 580 80 105 105 105 105 80
## 578 Azelf 580 75 125 70 125 70 115
## 579 Dialga 680 100 120 120 150 100 90
## 580 Palkia 680 90 120 100 150 120 100
## 581 Heatran 600 91 90 106 130 106 77
## 582 Regigigas 670 110 160 110 80 110 100
## 583 Giratina 680 150 100 120 100 120 90
## 584 Mega Giratina 680 150 120 100 120 100 90
## 585 Cresselia 600 120 70 120 75 130 85
## 586 Phione 480 80 80 80 80 80 80
## 587 Manaphy 600 100 100 100 100 100 100
## 588 Darkrai 600 70 90 90 135 90 125
## 589 Shaymin 600 100 100 100 100 100 100
## 590 Mega Shaymin 600 100 103 75 120 75 127
## 591 Arceus 720 120 120 120 120 120 120
## 592 Victini 600 100 100 100 100 100 100
## 593 Snivy 308 45 45 55 45 55 63
## 594 Servine 413 60 60 75 60 75 83
## 595 Serperior 528 75 75 95 75 95 113
## 596 Tepig 308 65 63 45 45 45 45
## 597 Pignite 418 90 93 55 70 55 55
## 598 Emboar 528 110 123 65 100 65 65
## 599 Oshawott 308 55 55 45 63 45 45
## 600 Dewott 413 75 75 60 83 60 60
## 601 Samurott 528 95 100 85 108 70 70
## 602 Patrat 255 45 55 39 35 39 42
## 603 Watchog 420 60 85 69 60 69 77
## 604 Lillipup 275 45 60 45 25 45 55
## 605 Herdier 370 65 80 65 35 65 60
## 606 Stoutland 500 85 110 90 45 90 80
## 607 Purrloin 281 41 50 37 50 37 66
## 608 Liepard 446 64 88 50 88 50 106
## 609 Pansage 316 50 53 48 53 48 64
## 610 Simisage 498 75 98 63 98 63 101
## 611 Pansear 316 50 53 48 53 48 64
## 612 Simisear 498 75 98 63 98 63 101
## 613 Panpour 316 50 53 48 53 48 64
## 614 Simipour 498 75 98 63 98 63 101
## 615 Munna 292 76 25 45 67 55 24
## 616 Musharna 487 116 55 85 107 95 29
## 617 Pidove 264 50 55 50 36 30 43
## 618 Tranquill 358 62 77 62 50 42 65
## 619 Unfezant 488 80 115 80 65 55 93
## 620 Blitzle 295 45 60 32 50 32 76
## 621 Zebstrika 497 75 100 63 80 63 116
## 622 Roggenrola 280 55 75 85 25 25 15
## 623 Boldore 390 70 105 105 50 40 20
## 624 Gigalith 515 85 135 130 60 80 25
## 625 Woobat 323 65 45 43 55 43 72
## 626 Swoobat 425 67 57 55 77 55 114
## 627 Drilbur 328 60 85 40 30 45 68
## 628 Excadrill 508 110 135 60 50 65 88
## 629 Audino 445 103 60 86 60 86 50
## 630 Mega Audino 545 103 60 126 80 126 50
## 631 Timburr 305 75 80 55 25 35 35
## 632 Gurdurr 405 85 105 85 40 50 40
## 633 Conkeldurr 505 105 140 95 55 65 45
## 634 Tympole 294 50 50 40 50 40 64
## 635 Palpitoad 384 75 65 55 65 55 69
## 636 Seismitoad 509 105 95 75 85 75 74
## 637 Throh 465 120 100 85 30 85 45
## 638 Sawk 465 75 125 75 30 75 85
## 639 Sewaddle 310 45 53 70 40 60 42
## 640 Swadloon 380 55 63 90 50 80 42
## 641 Leavanny 500 75 103 80 70 80 92
## 642 Venipede 260 30 45 59 30 39 57
## 643 Whirlipede 360 40 55 99 40 79 47
## 644 Scolipede 485 60 100 89 55 69 112
## 645 Cottonee 280 40 27 60 37 50 66
## 646 Whimsicott 480 60 67 85 77 75 116
## 647 Petilil 280 45 35 50 70 50 30
## 648 Lilligant 480 70 60 75 110 75 90
## 649 Basculin 460 70 92 65 80 55 98
## 650 Mega Basculin 460 70 92 65 80 55 98
## 651 Sandile 292 50 72 35 35 35 65
## 652 Krokorok 351 60 82 45 45 45 74
## 653 Krookodile 519 95 117 80 65 70 92
## 654 Darumaka 315 70 90 45 15 45 50
## 655 Mega Darumaka 315 70 90 45 15 45 50
## 656 Darmanitan 480 105 140 55 30 55 95
## 657 Mega Darmanitan 540 105 30 105 140 105 55
## 658 Mega Darmanitan X 480 105 140 55 30 55 95
## 659 Mega Darmanitan X 540 105 160 55 30 55 135
## 660 Maractus 461 75 86 67 106 67 60
## 661 Dwebble 325 50 65 85 35 35 55
## 662 Crustle 485 70 105 125 65 75 45
## 663 Scraggy 348 50 75 70 35 70 48
## 664 Scrafty 488 65 90 115 45 115 58
## 665 Sigilyph 490 72 58 80 103 80 97
## 666 Yamask 303 38 30 85 55 65 30
## 667 Mega Yamask 303 38 55 85 30 65 30
## 668 Cofagrigus 483 58 50 145 95 105 30
## 669 Tirtouga 355 54 78 103 53 45 22
## 670 Carracosta 495 74 108 133 83 65 32
## 671 Archen 401 55 112 45 74 45 70
## 672 Archeops 567 75 140 65 112 65 110
## 673 Trubbish 329 50 50 62 40 62 65
## 674 Garbodor 474 80 95 82 60 82 75
## 675 Zorua 330 40 65 40 80 40 65
## 676 Zoroark 510 60 105 60 120 60 105
## 677 Minccino 300 55 50 40 40 40 75
## 678 Cinccino 470 75 95 60 65 60 115
## 679 Gothita 290 45 30 50 55 65 45
## 680 Gothorita 390 60 45 70 75 85 55
## 681 Gothitelle 490 70 55 95 95 110 65
## 682 Solosis 290 45 30 40 105 50 20
## 683 Duosion 370 65 40 50 125 60 30
## 684 Reuniclus 490 110 65 75 125 85 30
## 685 Ducklett 305 62 44 50 44 50 55
## 686 Swanna 473 75 87 63 87 63 98
## 687 Vanillite 305 36 50 50 65 60 44
## 688 Vanillish 395 51 65 65 80 75 59
## 689 Vanilluxe 535 71 95 85 110 95 79
## 690 Deerling 335 60 60 50 40 50 75
## 691 Sawsbuck 475 80 100 70 60 70 95
## 692 Emolga 428 55 75 60 75 60 103
## 693 Karrablast 315 50 75 45 40 45 60
## 694 Escavalier 495 70 135 105 60 105 20
## 695 Foongus 294 69 55 45 55 55 15
## 696 Amoonguss 464 114 85 70 85 80 30
## 697 Frillish 335 55 40 50 65 85 40
## 698 Jellicent 480 100 60 70 85 105 60
## 699 Alomomola 470 165 75 80 40 45 65
## 700 Joltik 319 50 47 50 57 50 65
## 701 Galvantula 472 70 77 60 97 60 108
## 702 Ferroseed 305 44 50 91 24 86 10
## 703 Ferrothorn 489 74 94 131 54 116 20
## 704 Klink 300 40 55 70 45 60 30
## 705 Klang 440 60 80 95 70 85 50
## 706 Klinklang 520 60 100 115 70 85 90
## 707 Tynamo 275 35 55 40 45 40 60
## 708 Eelektrik 405 65 85 70 75 70 40
## 709 Eelektross 515 85 115 80 105 80 50
## 710 Elgyem 335 55 55 55 85 55 30
## 711 Beheeyem 485 75 75 75 125 95 40
## 712 Litwick 275 50 30 55 65 55 20
## 713 Lampent 370 60 40 60 95 60 55
## 714 Chandelure 520 60 55 90 145 90 80
## 715 Axew 320 46 87 60 30 40 57
## 716 Fraxure 410 66 117 70 40 50 67
## 717 Haxorus 540 76 147 90 60 70 97
## 718 Cubchoo 305 55 70 40 60 40 40
## 719 Beartic 505 95 130 80 70 80 50
## 720 Cryogonal 515 80 50 50 95 135 105
## 721 Shelmet 305 50 40 85 40 65 25
## 722 Accelgor 495 80 70 40 100 60 145
## 723 Stunfisk 471 109 66 84 81 99 32
## 724 Mega Stunfisk 471 109 81 99 66 84 32
## 725 Mienfoo 350 45 85 50 55 50 65
## 726 Mienshao 510 65 125 60 95 60 105
## 727 Druddigon 485 77 120 90 60 90 48
## 728 Golett 303 59 74 50 35 50 35
## 729 Golurk 483 89 124 80 55 80 55
## 730 Pawniard 340 45 85 70 40 40 60
## 731 Bisharp 490 65 125 100 60 70 70
## 732 Bouffalant 490 95 110 95 40 95 55
## 733 Rufflet 350 70 83 50 37 50 60
## 734 Braviary 510 100 123 75 57 75 80
## 735 Vullaby 370 70 55 75 45 65 60
## 736 Mandibuzz 510 110 65 105 55 95 80
## 737 Heatmor 484 85 97 66 105 66 65
## 738 Durant 484 58 109 112 48 48 109
## 739 Deino 300 52 65 50 45 50 38
## 740 Zweilous 420 72 85 70 65 70 58
## 741 Hydreigon 600 92 105 90 125 90 98
## 742 Larvesta 360 55 85 55 50 55 60
## 743 Volcarona 550 85 60 65 135 105 100
## 744 Cobalion 580 91 90 129 90 72 108
## 745 Terrakion 580 91 129 90 72 90 108
## 746 Virizion 580 91 90 72 90 129 108
## 747 Tornadus 580 79 115 70 125 80 111
## 748 Mega Tornadus 580 79 100 80 110 90 121
## 749 Thundurus 580 79 115 70 125 80 111
## 750 Mega Thundurus 580 79 105 70 145 80 101
## 751 Reshiram 680 100 120 100 150 120 90
## 752 Zekrom 680 100 150 120 120 100 90
## 753 Landorus 600 89 125 90 115 80 101
## 754 Mega Landorus 600 89 145 90 105 80 91
## 755 Kyurem 660 125 130 90 130 90 95
## 756 Mega Kyurem 700 125 170 100 120 90 95
## 757 Mega Kyurem X 700 125 120 90 170 100 95
## 758 Keldeo 580 91 72 90 129 90 108
## 759 Mega Keldeo 580 91 72 90 129 90 108
## 760 Meloetta 600 100 77 77 128 128 90
## 761 Mega Meloetta 600 100 128 90 77 77 128
## 762 Genesect 600 71 120 95 120 95 99
## 763 Chespin 313 56 61 65 48 45 38
## 764 Quilladin 405 61 78 95 56 58 57
## 765 Chesnaught 530 88 107 122 74 75 64
## 766 Fennekin 307 40 45 40 62 60 60
## 767 Braixen 409 59 59 58 90 70 73
## 768 Delphox 534 75 69 72 114 100 104
## 769 Froakie 314 41 56 40 62 44 71
## 770 Frogadier 405 54 63 52 83 56 97
## 771 Greninja 530 72 95 67 103 71 122
## 772 Mega Greninja 640 72 145 67 153 71 132
## 773 Bunnelby 237 38 36 38 32 36 57
## 774 Diggersby 423 85 56 77 50 77 78
## 775 Fletchling 278 45 50 43 40 38 62
## 776 Fletchinder 382 62 73 55 56 52 84
## 777 Talonflame 499 78 81 71 74 69 126
## 778 Scatterbug 200 38 35 40 27 25 35
## 779 Spewpa 213 45 22 60 27 30 29
## 780 Vivillon 411 80 52 50 90 50 89
## 781 Litleo 369 62 50 58 73 54 72
## 782 Pyroar 507 86 68 72 109 66 106
## 783 Flabébé 303 44 38 39 61 79 42
## 784 Floette 371 54 45 47 75 98 52
## 785 Florges 552 78 65 68 112 154 75
## 786 Skiddo 350 66 65 48 62 57 52
## 787 Gogoat 531 123 100 62 97 81 68
## 788 Pancham 348 67 82 62 46 48 43
## 789 Pangoro 495 95 124 78 69 71 58
## 790 Furfrou 472 75 80 60 65 90 102
## 791 Espurr 355 62 48 54 63 60 68
## 792 Meowstic 466 74 48 76 83 81 104
## 793 Mega Meowstic 466 74 48 76 83 81 104
## 794 Honedge 325 45 80 100 35 37 28
## 795 Doublade 448 59 110 150 45 49 35
## 796 Aegislash 500 60 50 140 50 140 60
## 797 Mega Aegislash 500 60 140 50 140 50 60
## 798 Spritzee 341 78 52 60 63 65 23
## 799 Aromatisse 462 101 72 72 99 89 29
## 800 Swirlix 341 62 48 66 59 57 49
## 801 Slurpuff 480 82 80 86 85 75 72
## 802 Inkay 288 53 54 53 37 46 45
## 803 Malamar 482 86 92 88 68 75 73
## 804 Binacle 306 42 52 67 39 56 50
## 805 Barbaracle 500 72 105 115 54 86 68
## 806 Skrelp 320 50 60 60 60 60 30
## 807 Dragalge 494 65 75 90 97 123 44
## 808 Clauncher 330 50 53 62 58 63 44
## 809 Clawitzer 500 71 73 88 120 89 59
## 810 Helioptile 289 44 38 33 61 43 70
## 811 Heliolisk 481 62 55 52 109 94 109
## 812 Tyrunt 362 58 89 77 45 45 48
## 813 Tyrantrum 521 82 121 119 69 59 71
## 814 Amaura 362 77 59 50 67 63 46
## 815 Aurorus 521 123 77 72 99 92 58
## 816 Sylveon 525 95 65 65 110 130 60
## 817 Hawlucha 500 78 92 75 74 63 118
## 818 Dedenne 431 67 58 57 81 67 101
## 819 Carbink 500 50 50 150 50 150 50
## 820 Goomy 300 45 50 35 55 75 40
## 821 Sliggoo 452 68 75 53 83 113 60
## 822 Goodra 600 90 100 70 110 150 80
## 823 Klefki 470 57 80 91 80 87 75
## 824 Phantump 309 43 70 48 50 60 38
## 825 Trevenant 474 85 110 76 65 82 56
## 826 Pumpkaboo 335 49 66 70 44 55 51
## 827 Mega Pumpkaboo 335 44 66 70 44 55 56
## 828 Mega Pumpkaboo X 335 54 66 70 44 55 46
## 829 Mega Pumpkaboo X 335 59 66 70 44 55 41
## 830 Gourgeist 494 65 90 122 58 75 84
## 831 Mega Gourgeist 494 55 85 122 58 75 99
## 832 Mega Gourgeist X 494 75 95 122 58 75 69
## 833 Mega Gourgeist X 494 85 100 122 58 75 54
## 834 Bergmite 304 55 69 85 32 35 28
## 835 Avalugg 514 95 117 184 44 46 28
## 836 Noibat 245 40 30 35 45 40 55
## 837 Noivern 535 85 70 80 97 80 123
## 838 Xerneas 680 126 131 95 131 98 99
## 839 Yveltal 680 126 131 95 131 98 99
## 840 Zygarde 600 108 100 121 81 95 95
## 841 Mega Zygarde 486 54 100 71 61 85 115
## 842 Mega Zygarde X 708 216 100 121 91 95 85
## 843 Diancie 600 50 100 150 100 150 50
## 844 Mega Diancie 700 50 160 110 160 110 110
## 845 Hoopa 600 80 110 60 150 130 70
## 846 Mega Hoopa 680 80 160 60 170 130 80
## 847 Volcanion 600 80 110 120 130 90 70
## 848 Rowlet 320 68 55 55 50 50 42
## 849 Dartrix 420 78 75 75 70 70 52
## 850 Decidueye 530 78 107 75 100 100 70
## 851 Litten 320 45 65 40 60 40 70
## 852 Torracat 420 65 85 50 80 50 90
## 853 Incineroar 530 95 115 90 80 90 60
## 854 Popplio 320 50 54 54 66 56 40
## 855 Brionne 420 60 69 69 91 81 50
## 856 Primarina 530 80 74 74 126 116 60
## 857 Pikipek 265 35 75 30 30 30 65
## 858 Trumbeak 355 55 85 50 40 50 75
## 859 Toucannon 485 80 120 75 75 75 60
## 860 Yungoos 253 48 70 30 30 30 45
## 861 Gumshoos 418 88 110 60 55 60 45
## 862 Grubbin 300 47 62 45 55 45 46
## 863 Charjabug 400 57 82 95 55 75 36
## 864 Vikavolt 500 77 70 90 145 75 43
## 865 Crabrawler 338 47 82 57 42 47 63
## 866 Crabominable 478 97 132 77 62 67 43
## 867 Oricorio 476 75 70 70 98 70 93
## 868 Mega Oricorio 476 75 70 70 98 70 93
## 869 Mega Oricorio X 476 75 70 70 98 70 93
## 870 Mega Oricorio X 476 75 70 70 98 70 93
## 871 Cutiefly 304 40 45 40 55 40 84
## 872 Ribombee 464 60 55 60 95 70 124
## 873 Rockruff 280 45 65 40 30 40 60
## 874 Mega Rockruff 280 45 65 40 30 40 60
## 875 Lycanroc 487 75 115 65 55 65 112
## 876 Mega Lycanroc 487 85 115 75 55 75 82
## 877 Mega Lycanroc X 487 75 117 65 55 65 110
## 878 Wishiwashi 175 45 20 20 25 25 40
## 879 Mega Wishiwashi 620 45 140 130 140 135 30
## 880 Mareanie 305 50 53 62 43 52 45
## 881 Toxapex 495 50 63 152 53 142 35
## 882 Mudbray 385 70 100 70 45 55 45
## 883 Mudsdale 500 100 125 100 55 85 35
## 884 Dewpider 269 38 40 52 40 72 27
## 885 Araquanid 454 68 70 92 50 132 42
## 886 Fomantis 250 40 55 35 50 35 35
## 887 Lurantis 480 70 105 90 80 90 45
## 888 Morelull 285 40 35 55 65 75 15
## 889 Shiinotic 405 60 45 80 90 100 30
## 890 Salandit 320 48 44 40 71 40 77
## 891 Salazzle 480 68 64 60 111 60 117
## 892 Stufful 340 70 75 50 45 50 50
## 893 Bewear 500 120 125 80 55 60 60
## 894 Bounsweet 210 42 30 38 30 38 32
## 895 Steenee 290 52 40 48 40 48 62
## 896 Tsareena 510 72 120 98 50 98 72
## 897 Comfey 485 51 52 90 82 110 100
## 898 Oranguru 490 90 60 80 90 110 60
## 899 Passimian 490 100 120 90 40 60 80
## 900 Wimpod 230 25 35 40 20 30 80
## 901 Golisopod 530 75 125 140 60 90 40
## 902 Sandygast 320 55 55 80 70 45 15
## 903 Palossand 480 85 75 110 100 75 35
## 904 Pyukumuku 410 55 60 130 30 130 5
## 905 Type: Null 534 95 95 95 95 95 59
## 906 Silvally 570 95 95 95 95 95 95
## 907 Minior 440 60 60 100 60 100 60
## 908 Mega Minior 500 60 100 60 100 60 120
## 909 Komala 480 65 115 65 75 95 65
## 910 Turtonator 485 60 78 135 91 85 36
## 911 Togedemaru 435 65 98 63 40 73 96
## 912 Mimikyu 476 55 90 80 50 105 96
## 913 Bruxish 475 68 105 70 70 70 92
## 914 Drampa 485 78 60 85 135 91 36
## 915 Dhelmise 517 70 131 100 86 90 40
## 916 Jangmo-o 300 45 55 65 45 45 45
## 917 Hakamo-o 420 55 75 90 65 70 65
## 918 Kommo-o 600 75 110 125 100 105 85
## 919 Tapu Koko 570 70 115 85 95 75 130
## 920 Tapu Lele 570 70 85 75 130 115 95
## 921 Tapu Bulu 570 70 130 115 85 95 75
## 922 Tapu Fini 570 70 75 115 95 130 85
## 923 Cosmog 200 43 29 31 29 31 37
## 924 Cosmoem 400 43 29 131 29 131 37
## 925 Solgaleo 680 137 137 107 113 89 97
## 926 Lunala 680 137 113 89 137 107 97
## 927 Nihilego 570 109 53 47 127 131 103
## 928 Buzzwole 570 107 139 139 53 53 79
## 929 Pheromosa 570 71 137 37 137 37 151
## 930 Xurkitree 570 83 89 71 173 71 83
## 931 Celesteela 570 97 101 103 107 101 61
## 932 Kartana 570 59 181 131 59 31 109
## 933 Guzzlord 570 223 101 53 97 53 43
## 934 Necrozma 600 97 107 101 127 89 79
## 935 Mega Necrozma 680 97 157 127 113 109 77
## 936 Mega Necrozma X 680 97 113 109 157 127 77
## 937 Mega Necrozma X 754 97 167 97 167 97 129
## 938 Magearna 600 80 95 115 130 115 65
## 939 Marshadow 600 90 125 80 90 90 125
## 940 Poipole 420 67 73 67 73 67 73
## 941 Naganadel 540 73 73 73 127 73 121
## 942 Stakataka 570 61 131 211 53 101 13
## 943 Blacephalon 570 53 127 53 151 79 107
## 944 Zeraora 600 88 112 75 102 80 143
## 945 Meltan 300 46 65 65 55 35 34
## 946 Melmetal 600 135 143 143 80 65 34
## 947 Grookey 310 50 65 50 40 40 65
## 948 Thwackey 420 70 85 70 55 60 80
## 949 Rillaboom 530 100 125 90 60 70 85
## 950 Scorbunny 310 50 71 40 40 40 69
## 951 Raboot 420 65 86 60 55 60 94
## 952 Cinderace 530 80 116 75 65 75 119
## 953 Sobble 310 50 40 40 70 40 70
## 954 Drizzile 420 65 60 55 95 55 90
## 955 Inteleon 530 70 85 65 125 65 120
## 956 Skwovet 275 70 55 55 35 35 25
## 957 Greedent 460 120 95 95 55 75 20
## 958 Rookidee 245 38 47 35 33 35 57
## 959 Corvisquire 365 68 67 55 43 55 77
## 960 Corviknight 495 98 87 105 53 85 67
## 961 Blipbug 180 25 20 20 25 45 45
## 962 Dottler 335 50 35 80 50 90 30
## 963 Orbeetle 505 60 45 110 80 120 90
## 964 Nickit 245 40 28 28 47 52 50
## 965 Thievul 455 70 58 58 87 92 90
## 966 Gossifleur 250 40 40 60 40 60 10
## 967 Eldegoss 460 60 50 90 80 120 60
## 968 Wooloo 270 42 40 55 40 45 48
## 969 Dubwool 490 72 80 100 60 90 88
## 970 Chewtle 284 50 64 50 38 38 44
## 971 Drednaw 485 90 115 90 48 68 74
## 972 Yamper 270 59 45 50 40 50 26
## 973 Boltund 490 69 90 60 90 60 121
## 974 Rolycoly 240 30 40 50 40 50 30
## 975 Carkol 410 80 60 90 60 70 50
## 976 Coalossal 510 110 80 120 80 90 30
## 977 Applin 260 40 40 80 40 40 20
## 978 Flapple 485 70 110 80 95 60 70
## 979 Appletun 485 110 85 80 100 80 30
## 980 Silicobra 315 52 57 75 35 50 46
## 981 Sandaconda 510 72 107 125 65 70 71
## 982 Cramorant 475 70 85 55 85 95 85
## 983 Arrokuda 280 41 63 40 40 30 66
## 984 Barraskewda 490 61 123 60 60 50 136
## 985 Toxel 242 40 38 35 54 35 40
## 986 Toxtricity 502 75 98 70 114 70 75
## 987 Mega Toxtricity 502 75 98 70 114 70 75
## 988 Sizzlipede 305 50 65 45 50 50 45
## 989 Centiskorch 525 100 115 65 90 90 65
## 990 Clobbopus 310 50 68 60 50 50 32
## 991 Grapploct 480 80 118 90 70 80 42
## 992 Sinistea 308 40 45 45 74 54 50
## 993 Polteageist 508 60 65 65 134 114 70
## 994 Hatenna 265 42 30 45 56 53 39
## 995 Hattrem 370 57 40 65 86 73 49
## 996 Hatterene 510 57 90 95 136 103 29
## 997 Impidimp 265 45 45 30 55 40 50
## 998 Morgrem 370 65 60 45 75 55 70
## 999 Grimmsnarl 510 95 120 65 95 75 60
## 1000 Obstagoon 520 93 90 101 60 81 95
## 1001 Perrserker 440 70 110 100 50 60 50
## 1002 Cursola 510 60 95 50 145 130 30
## 1003 Sirfetch'd 507 62 135 95 68 82 65
## 1004 Mr. Rime 520 80 85 75 110 100 70
## 1005 Runerigus 483 58 95 145 50 105 30
## 1006 Milcery 270 45 40 40 50 61 34
## 1007 Alcremie 495 65 60 75 110 121 64
## 1008 Falinks 470 65 100 100 70 60 75
## 1009 Pincurchin 435 48 101 95 91 85 15
## 1010 Snom 185 30 25 35 45 30 20
## 1011 Frosmoth 475 70 65 60 125 90 65
## 1012 Stonjourner 470 100 125 135 20 20 70
## 1013 Eiscue 470 75 80 110 65 90 50
## 1014 Mega Eiscue 470 75 80 70 65 50 130
## 1015 Indeedee 475 60 65 55 105 95 95
## 1016 Mega Indeedee 475 70 55 65 95 105 85
## 1017 Morpeko 436 58 95 58 70 58 97
## 1018 Mega Morpeko 436 58 95 58 70 58 97
## 1019 Cufant 330 72 80 49 40 49 40
## 1020 Copperajah 500 122 130 69 80 69 30
## 1021 Dracozolt 505 90 100 90 80 70 75
## 1022 Arctozolt 505 90 100 90 90 80 55
## 1023 Dracovish 505 90 90 100 70 80 75
## 1024 Arctovish 505 90 90 100 80 90 55
## 1025 Duraludon 535 70 95 115 120 50 85
## 1026 Dreepy 270 28 60 30 40 30 82
## 1027 Drakloak 410 68 80 50 60 50 102
## 1028 Dragapult 600 88 120 75 100 75 142
## 1029 Zacian 720 92 170 115 80 115 148
## 1030 Mega Zacian 670 92 130 115 80 115 138
## 1031 Zamazenta 720 92 130 145 80 145 128
## 1032 Mega Zamazenta 670 92 130 115 80 115 138
## 1033 Eternatus 690 140 85 95 145 95 130
## 1034 Mega Eternatus 1125 255 115 250 125 250 130
## 1035 Kubfu 385 60 90 60 53 50 72
## 1036 Urshifu 550 100 130 100 63 60 97
## 1037 Mega Urshifu 550 100 130 100 63 60 97
## 1038 Zarude 600 105 120 105 70 95 105
## 1039 Regieleki 580 80 100 50 100 50 200
## 1040 Regidrago 580 200 100 50 100 50 80
## 1041 Glastrier 580 100 145 130 65 110 30
## 1042 Spectrier 580 100 65 60 145 80 130
## 1043 Calyrex 500 100 80 80 80 80 80
## 1044 Mega Calyrex 680 100 165 150 85 130 50
## 1045 Mega Calyrex X 680 100 85 80 165 100 150length(d2)## [1] 8head(d2)tail(d2)str(d2)## 'data.frame': 1045 obs. of 8 variables:
## $ Name : chr "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur" ...
## $ Total : int 318 405 525 625 309 405 534 634 634 314 ...
## $ HP : int 45 60 80 80 39 58 78 78 78 44 ...
## $ Attack : int 49 62 82 100 52 64 84 130 104 48 ...
## $ Defence : int 49 63 83 123 43 58 78 111 78 65 ...
## $ Sp_attack : int 65 80 100 122 60 80 109 130 159 50 ...
## $ Sp_defence: int 65 80 100 120 50 65 85 85 115 64 ...
## $ Speed : int 45 60 80 80 65 80 100 100 100 43 ...d2$Sp_defence## [1] 65 80 100 120 50 65 85 85 115 64 80 105 115 20 25 80 20 25
## [19] 80 80 35 50 70 80 35 35 70 80 31 61 54 79 50 60 80 85
## [37] 30 35 55 65 40 55 85 40 55 75 65 90 65 65 100 100 25 50
## [55] 40 75 65 75 90 55 80 55 75 45 45 70 70 40 40 40 65 65
## [73] 50 80 45 70 50 80 40 50 90 55 70 95 105 35 60 85 30 45
## [91] 70 100 120 30 30 45 45 65 65 65 65 80 80 40 40 80 80 70
## [109] 55 70 62 62 35 60 70 95 50 50 100 100 25 45 35 55 75 95
## [127] 45 90 115 25 50 55 80 45 75 75 50 80 80 110 110 75 45 70
## [145] 70 30 45 105 40 80 100 25 45 50 80 55 85 120 90 80 95 85
## [163] 85 70 90 70 20 100 130 95 48 65 85 95 95 110 75 55 70 45
## [181] 70 75 95 110 125 100 90 90 85 125 50 70 100 90 100 120 100 65
## [199] 80 100 50 65 85 48 63 83 45 55 56 96 80 110 40 70 80 56
## [217] 76 35 55 20 65 105 45 70 45 60 90 110 100 50 80 65 100 55
## [235] 65 95 55 30 85 45 25 65 95 130 42 110 110 85 48 58 65 35
## [253] 60 65 65 65 95 40 60 55 80 100 230 95 105 75 50 75 40 80
## [271] 30 60 95 100 35 75 45 140 70 50 80 90 95 40 60 95 65 45
## [289] 35 110 65 55 55 70 135 100 75 115 50 70 100 120 154 154 100 55
## [307] 65 85 85 50 60 70 80 50 70 90 110 30 60 41 41 61 61 30
## [325] 25 50 25 90 50 70 100 30 40 60 30 50 30 70 35 55 115 135
## [343] 52 82 60 60 35 55 65 30 50 30 23 43 73 30 60 40 90 35
## [361] 55 65 115 55 95 40 50 60 80 55 75 85 40 60 80 75 85 85
## [379] 85 80 53 83 20 40 65 35 45 45 75 105 70 80 110 60 45 50
## [397] 80 40 60 75 105 105 60 60 85 65 41 71 35 55 70 120 87 107
## [415] 50 80 55 125 70 70 70 70 120 33 63 83 90 130 87 90 60 60
## [433] 48 50 80 80 50 70 90 55 75 75 65 65 30 50 80 90 60 80
## [451] 90 110 100 200 150 130 150 110 120 140 160 90 90 90 100 100 50 20
## [469] 160 90 55 65 85 44 52 71 56 76 101 30 40 60 40 60 41 51
## [487] 34 49 79 70 105 30 50 88 138 45 105 85 95 50 42 102 90 30
## [505] 50 53 78 62 82 66 44 54 56 96 96 105 52 37 59 50 41 61
## [523] 86 116 45 90 65 42 108 45 55 85 95 85 40 70 70 42 72 55
## [541] 75 40 65 72 61 86 120 60 85 105 85 90 95 55 50 85 95 115
## [559] 56 65 95 75 60 75 115 115 150 135 70 77 107 107 107 107 107 130
## [577] 105 70 100 120 106 110 120 100 130 80 100 90 100 75 120 100 55 75
## [595] 95 45 55 65 45 60 70 39 69 45 65 90 37 50 48 63 48 63
## [613] 48 63 55 95 30 42 55 32 63 25 40 80 43 55 45 65 86 126
## [631] 35 50 65 40 55 75 85 75 60 80 80 39 79 69 50 75 50 75
## [649] 55 55 35 45 70 45 45 55 105 55 55 67 35 75 70 115 80 65
## [667] 65 105 45 65 45 65 62 82 40 60 40 60 65 85 110 50 60 85
## [685] 50 63 60 75 95 50 70 60 45 105 55 80 85 105 45 50 60 86
## [703] 116 60 85 85 40 70 80 55 95 55 60 90 40 50 70 40 80 135
## [721] 65 60 99 84 50 60 90 50 80 40 70 95 50 75 65 95 66 48
## [739] 50 70 90 55 105 72 90 129 80 90 80 80 120 100 80 80 90 90
## [757] 100 90 90 128 77 95 45 58 75 60 70 100 44 56 71 71 36 77
## [775] 38 52 69 25 30 50 54 66 79 98 154 57 81 48 71 90 60 81
## [793] 81 37 49 140 50 65 89 57 75 46 75 56 86 60 123 63 89 43
## [811] 94 45 59 63 92 130 63 67 150 75 113 150 87 60 82 55 55 55
## [829] 55 75 75 75 75 35 46 40 80 98 98 95 85 95 150 110 130 130
## [847] 90 50 70 100 40 50 90 56 81 116 30 50 75 30 60 45 75 75
## [865] 47 67 70 70 70 70 40 70 40 40 65 75 65 25 135 52 142 55
## [883] 85 72 132 35 90 75 100 40 60 50 60 38 48 98 110 110 60 30
## [901] 90 45 75 130 95 95 100 60 95 85 73 105 70 91 90 45 70 105
## [919] 75 115 95 130 31 131 89 107 131 53 37 71 101 31 53 89 109 127
## [937] 97 115 90 67 73 101 79 80 35 65 40 60 70 40 60 75 40 55
## [955] 65 35 75 35 55 85 45 90 120 52 92 60 120 45 90 38 68 50
## [973] 60 50 70 90 40 60 80 50 70 95 30 50 35 70 70 50 90 50
## [991] 80 54 114 53 73 103 40 55 75 81 60 130 82 100 105 61 121 60
## [1009] 85 30 90 20 90 50 95 105 58 58 49 69 70 80 80 90 50 30
## [1027] 50 75 115 115 145 115 95 250 50 60 60 95 50 50 110 80 80 130
## [1045] 100d2$d2## NULLcbind(d2)dimnames(d2)## [[1]]
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
## [11] "11" "12" "13" "14" "15" "16" "17" "18" "19" "20"
## [21] "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
## [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40"
## [41] "41" "42" "43" "44" "45" "46" "47" "48" "49" "50"
## [51] "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70"
## [71] "71" "72" "73" "74" "75" "76" "77" "78" "79" "80"
## [81] "81" "82" "83" "84" "85" "86" "87" "88" "89" "90"
## [91] "91" "92" "93" "94" "95" "96" "97" "98" "99" "100"
## [101] "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
## [111] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130"
## [131] "131" "132" "133" "134" "135" "136" "137" "138" "139" "140"
## [141] "141" "142" "143" "144" "145" "146" "147" "148" "149" "150"
## [151] "151" "152" "153" "154" "155" "156" "157" "158" "159" "160"
## [161] "161" "162" "163" "164" "165" "166" "167" "168" "169" "170"
## [171] "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190"
## [191] "191" "192" "193" "194" "195" "196" "197" "198" "199" "200"
## [201] "201" "202" "203" "204" "205" "206" "207" "208" "209" "210"
## [211] "211" "212" "213" "214" "215" "216" "217" "218" "219" "220"
## [221] "221" "222" "223" "224" "225" "226" "227" "228" "229" "230"
## [231] "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
## [241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250"
## [251] "251" "252" "253" "254" "255" "256" "257" "258" "259" "260"
## [261] "261" "262" "263" "264" "265" "266" "267" "268" "269" "270"
## [271] "271" "272" "273" "274" "275" "276" "277" "278" "279" "280"
## [281] "281" "282" "283" "284" "285" "286" "287" "288" "289" "290"
## [291] "291" "292" "293" "294" "295" "296" "297" "298" "299" "300"
## [301] "301" "302" "303" "304" "305" "306" "307" "308" "309" "310"
## [311] "311" "312" "313" "314" "315" "316" "317" "318" "319" "320"
## [321] "321" "322" "323" "324" "325" "326" "327" "328" "329" "330"
## [331] "331" "332" "333" "334" "335" "336" "337" "338" "339" "340"
## [341] "341" "342" "343" "344" "345" "346" "347" "348" "349" "350"
## [351] "351" "352" "353" "354" "355" "356" "357" "358" "359" "360"
## [361] "361" "362" "363" "364" "365" "366" "367" "368" "369" "370"
## [371] "371" "372" "373" "374" "375" "376" "377" "378" "379" "380"
## [381] "381" "382" "383" "384" "385" "386" "387" "388" "389" "390"
## [391] "391" "392" "393" "394" "395" "396" "397" "398" "399" "400"
## [401] "401" "402" "403" "404" "405" "406" "407" "408" "409" "410"
## [411] "411" "412" "413" "414" "415" "416" "417" "418" "419" "420"
## [421] "421" "422" "423" "424" "425" "426" "427" "428" "429" "430"
## [431] "431" "432" "433" "434" "435" "436" "437" "438" "439" "440"
## [441] "441" "442" "443" "444" "445" "446" "447" "448" "449" "450"
## [451] "451" "452" "453" "454" "455" "456" "457" "458" "459" "460"
## [461] "461" "462" "463" "464" "465" "466" "467" "468" "469" "470"
## [471] "471" "472" "473" "474" "475" "476" "477" "478" "479" "480"
## [481] "481" "482" "483" "484" "485" "486" "487" "488" "489" "490"
## [491] "491" "492" "493" "494" "495" "496" "497" "498" "499" "500"
## [501] "501" "502" "503" "504" "505" "506" "507" "508" "509" "510"
## [511] "511" "512" "513" "514" "515" "516" "517" "518" "519" "520"
## [521] "521" "522" "523" "524" "525" "526" "527" "528" "529" "530"
## [531] "531" "532" "533" "534" "535" "536" "537" "538" "539" "540"
## [541] "541" "542" "543" "544" "545" "546" "547" "548" "549" "550"
## [551] "551" "552" "553" "554" "555" "556" "557" "558" "559" "560"
## [561] "561" "562" "563" "564" "565" "566" "567" "568" "569" "570"
## [571] "571" "572" "573" "574" "575" "576" "577" "578" "579" "580"
## [581] "581" "582" "583" "584" "585" "586" "587" "588" "589" "590"
## [591] "591" "592" "593" "594" "595" "596" "597" "598" "599" "600"
## [601] "601" "602" "603" "604" "605" "606" "607" "608" "609" "610"
## [611] "611" "612" "613" "614" "615" "616" "617" "618" "619" "620"
## [621] "621" "622" "623" "624" "625" "626" "627" "628" "629" "630"
## [631] "631" "632" "633" "634" "635" "636" "637" "638" "639" "640"
## [641] "641" "642" "643" "644" "645" "646" "647" "648" "649" "650"
## [651] "651" "652" "653" "654" "655" "656" "657" "658" "659" "660"
## [661] "661" "662" "663" "664" "665" "666" "667" "668" "669" "670"
## [671] "671" "672" "673" "674" "675" "676" "677" "678" "679" "680"
## [681] "681" "682" "683" "684" "685" "686" "687" "688" "689" "690"
## [691] "691" "692" "693" "694" "695" "696" "697" "698" "699" "700"
## [701] "701" "702" "703" "704" "705" "706" "707" "708" "709" "710"
## [711] "711" "712" "713" "714" "715" "716" "717" "718" "719" "720"
## [721] "721" "722" "723" "724" "725" "726" "727" "728" "729" "730"
## [731] "731" "732" "733" "734" "735" "736" "737" "738" "739" "740"
## [741] "741" "742" "743" "744" "745" "746" "747" "748" "749" "750"
## [751] "751" "752" "753" "754" "755" "756" "757" "758" "759" "760"
## [761] "761" "762" "763" "764" "765" "766" "767" "768" "769" "770"
## [771] "771" "772" "773" "774" "775" "776" "777" "778" "779" "780"
## [781] "781" "782" "783" "784" "785" "786" "787" "788" "789" "790"
## [791] "791" "792" "793" "794" "795" "796" "797" "798" "799" "800"
## [801] "801" "802" "803" "804" "805" "806" "807" "808" "809" "810"
## [811] "811" "812" "813" "814" "815" "816" "817" "818" "819" "820"
## [821] "821" "822" "823" "824" "825" "826" "827" "828" "829" "830"
## [831] "831" "832" "833" "834" "835" "836" "837" "838" "839" "840"
## [841] "841" "842" "843" "844" "845" "846" "847" "848" "849" "850"
## [851] "851" "852" "853" "854" "855" "856" "857" "858" "859" "860"
## [861] "861" "862" "863" "864" "865" "866" "867" "868" "869" "870"
## [871] "871" "872" "873" "874" "875" "876" "877" "878" "879" "880"
## [881] "881" "882" "883" "884" "885" "886" "887" "888" "889" "890"
## [891] "891" "892" "893" "894" "895" "896" "897" "898" "899" "900"
## [901] "901" "902" "903" "904" "905" "906" "907" "908" "909" "910"
## [911] "911" "912" "913" "914" "915" "916" "917" "918" "919" "920"
## [921] "921" "922" "923" "924" "925" "926" "927" "928" "929" "930"
## [931] "931" "932" "933" "934" "935" "936" "937" "938" "939" "940"
## [941] "941" "942" "943" "944" "945" "946" "947" "948" "949" "950"
## [951] "951" "952" "953" "954" "955" "956" "957" "958" "959" "960"
## [961] "961" "962" "963" "964" "965" "966" "967" "968" "969" "970"
## [971] "971" "972" "973" "974" "975" "976" "977" "978" "979" "980"
## [981] "981" "982" "983" "984" "985" "986" "987" "988" "989" "990"
## [991] "991" "992" "993" "994" "995" "996" "997" "998" "999" "1000"
## [1001] "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008" "1009" "1010"
## [1011] "1011" "1012" "1013" "1014" "1015" "1016" "1017" "1018" "1019" "1020"
## [1021] "1021" "1022" "1023" "1024" "1025" "1026" "1027" "1028" "1029" "1030"
## [1031] "1031" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1040"
## [1041] "1041" "1042" "1043" "1044" "1045"
##
## [[2]]
## [1] "Name" "Total" "HP" "Attack" "Defence"
## [6] "Sp_attack" "Sp_defence" "Speed"names(d2)## [1] "Name" "Total" "HP" "Attack" "Defence"
## [6] "Sp_attack" "Sp_defence" "Speed"mean(d2$Attack)## [1] 80.46699max(d2$HP)## [1] 255quantile(d2$Sp_attack)## 0% 25% 50% 75% 100%
## 10 50 65 95 194fivenum(d2$Defence)## [1] 5 50 70 90 250Cumulative statistics:
cumsum(d2$HP)## [1] 45 105 185 265 304 362 440 518 596 640 699 778
## [13] 857 902 952 1012 1052 1097 1162 1227 1267 1330 1413 1496
## [25] 1526 1556 1611 1686 1726 1791 1826 1886 1921 1966 2026 2086
## [37] 2136 2186 2261 2336 2391 2461 2551 2597 2658 2739 2809 2904
## [49] 2942 2980 3053 3126 3241 3381 3421 3496 3541 3601 3676 3711
## [61] 3771 3831 3901 3911 3921 3956 3991 4031 4071 4121 4186 4251
## [73] 4301 4381 4421 4486 4541 4631 4671 4736 4826 4851 4891 4946
## [85] 5001 5071 5151 5241 5291 5356 5436 5476 5556 5596 5636 5691
## [97] 5746 5826 5906 5956 6006 6071 6136 6226 6316 6411 6506 6601
## [109] 6626 6676 6728 6780 6815 6875 6940 7030 7110 7190 7295 7400
## [121] 7430 7480 7510 7555 7615 7675 7710 7770 7855 7885 7940 7980
## [133] 8040 8100 8195 8290 8340 8400 8460 8510 8560 8650 8690 8755
## [145] 8820 8900 9005 9255 9320 9425 9530 9560 9615 9660 9740 9770
## [157] 9830 9870 9920 9990 10055 10120 10185 10250 10315 10390 10410 10505
## [169] 10600 10730 10778 10833 10898 11028 11093 11158 11223 11258 11328 11358
## [181] 11418 11498 11578 11738 11828 11918 12008 12098 12188 12278 12319 12380
## [193] 12471 12577 12683 12789 12889 12934 12994 13074 13113 13171 13249 13299
## [205] 13364 13449 13484 13569 13629 13729 13769 13824 13864 13934 14019 14094
## [217] 14219 14239 14289 14379 14414 14469 14509 14574 14629 14699 14789 14879
## [229] 14954 15024 15124 15194 15284 15319 15374 15449 15504 15534 15609 15674
## [241] 15729 15824 15889 15984 16044 16139 16234 16294 16342 16532 16602 16652
## [253] 16727 16827 16892 16967 17042 17102 17192 17257 17327 17397 17417 17497
## [265] 17577 17632 17692 17782 17822 17882 17932 18032 18097 18157 18192 18267
## [277] 18312 18397 18462 18507 18582 18657 18732 18822 18912 18997 19070 19125
## [289] 19160 19210 19255 19300 19345 19440 19695 19785 19900 20000 20050 20120
## [301] 20220 20320 20426 20532 20632 20672 20722 20792 20862 20907 20967 21047
## [313] 21127 21177 21247 21347 21447 21482 21552 21590 21628 21706 21784 21829
## [325] 21879 21939 21989 22049 22089 22149 22229 22269 22339 22429 22469 22529
## [337] 22569 22629 22657 22695 22763 22831 22871 22941 23001 23061 23121 23201
## [349] 23351 23382 23443 23444 23508 23592 23696 23768 23912 23962 23992 24042
## [361] 24112 24162 24212 24262 24312 24362 24422 24492 24562 24592 24652 24712
## [373] 24752 24822 24892 24952 25012 25077 25142 25192 25262 25362 25407 25477
## [385] 25547 25677 25847 25907 25977 26047 26117 26177 26257 26317 26362 26412
## [397] 26492 26542 26612 26657 26732 26807 26880 26953 27043 27133 27183 27293
## [409] 27336 27399 27439 27499 27565 27651 27696 27771 27791 27886 27956 28026
## [421] 28096 28166 28226 28270 28334 28398 28418 28458 28557 28632 28697 28762
## [433] 28857 28907 28987 29067 29137 29227 29337 29372 29427 29482 29582 29625
## [445] 29670 29735 29830 29925 29965 30025 30105 30185 30265 30345 30425 30505
## [457] 30585 30665 30745 30845 30945 31045 31145 31250 31355 31455 31505 31555
## [469] 31605 31655 31710 31785 31880 31924 31988 32064 32117 32181 32265 32305
## [481] 32360 32445 32504 32583 32620 32697 32742 32802 32882 32922 32982 33049
## [493] 33146 33176 33236 33276 33336 33396 33456 33526 33556 33626 33686 33741
## [505] 33826 33871 33941 34017 34128 34203 34293 34443 34498 34563 34628 34688
## [517] 34788 34837 34908 34953 35016 35119 35176 35243 35293 35313 35413 35489
## [529] 35539 35597 35665 35773 35881 36016 36056 36126 36196 36264 36372 36412
## [541] 36482 36530 36613 36687 36736 36805 36850 36910 37000 37090 37160 37230
## [553] 37340 37455 37555 37630 37705 37790 37876 37941 38006 38081 38191 38276
## [565] 38344 38412 38472 38517 38587 38637 38687 38737 38787 38837 38887 38962
## [577] 39042 39117 39217 39307 39398 39508 39658 39808 39928 40008 40108 40178
## [589] 40278 40378 40498 40598 40643 40703 40778 40843 40933 41043 41098 41173
## [601] 41268 41313 41373 41418 41483 41568 41609 41673 41723 41798 41848 41923
## [613] 41973 42048 42124 42240 42290 42352 42432 42477 42552 42607 42677 42762
## [625] 42827 42894 42954 43064 43167 43270 43345 43430 43535 43585 43660 43765
## [637] 43885 43960 44005 44060 44135 44165 44205 44265 44305 44365 44410 44480
## [649] 44550 44620 44670 44730 44825 44895 44965 45070 45175 45280 45385 45460
## [661] 45510 45580 45630 45695 45767 45805 45843 45901 45955 46029 46084 46159
## [673] 46209 46289 46329 46389 46444 46519 46564 46624 46694 46739 46804 46914
## [685] 46976 47051 47087 47138 47209 47269 47349 47404 47454 47524 47593 47707
## [697] 47762 47862 48027 48077 48147 48191 48265 48305 48365 48425 48460 48525
## [709] 48610 48665 48740 48790 48850 48910 48956 49022 49098 49153 49248 49328
## [721] 49378 49458 49567 49676 49721 49786 49863 49922 50011 50056 50121 50216
## [733] 50286 50386 50456 50566 50651 50709 50761 50833 50925 50980 51065 51156
## [745] 51247 51338 51417 51496 51575 51654 51754 51854 51943 52032 52157 52282
## [757] 52407 52498 52589 52689 52789 52860 52916 52977 53065 53105 53164 53239
## [769] 53280 53334 53406 53478 53516 53601 53646 53708 53786 53824 53869 53949
## [781] 54011 54097 54141 54195 54273 54339 54462 54529 54624 54699 54761 54835
## [793] 54909 54954 55013 55073 55133 55211 55312 55374 55456 55509 55595 55637
## [805] 55709 55759 55824 55874 55945 55989 56051 56109 56191 56268 56391 56486
## [817] 56564 56631 56681 56726 56794 56884 56941 56984 57069 57118 57162 57216
## [829] 57275 57340 57395 57470 57555 57610 57705 57745 57830 57956 58082 58190
## [841] 58244 58460 58510 58560 58640 58720 58800 58868 58946 59024 59069 59134
## [853] 59229 59279 59339 59419 59454 59509 59589 59637 59725 59772 59829 59906
## [865] 59953 60050 60125 60200 60275 60350 60390 60450 60495 60540 60615 60700
## [877] 60775 60820 60865 60915 60965 61035 61135 61173 61241 61281 61351 61391
## [889] 61451 61499 61567 61637 61757 61799 61851 61923 61974 62064 62164 62189
## [901] 62264 62319 62404 62459 62554 62649 62709 62769 62834 62894 62959 63014
## [913] 63082 63160 63230 63275 63330 63405 63475 63545 63615 63685 63728 63771
## [925] 63908 64045 64154 64261 64332 64415 64512 64571 64794 64891 64988 65085
## [937] 65182 65262 65352 65419 65492 65553 65606 65694 65740 65875 65925 65995
## [949] 66095 66145 66210 66290 66340 66405 66475 66545 66665 66703 66771 66869
## [961] 66894 66944 67004 67044 67114 67154 67214 67256 67328 67378 67468 67527
## [973] 67596 67626 67706 67816 67856 67926 68036 68088 68160 68230 68271 68332
## [985] 68372 68447 68522 68572 68672 68722 68802 68842 68902 68944 69001 69058
## [997] 69103 69168 69263 69356 69426 69486 69548 69628 69686 69731 69796 69861
## [1009] 69909 69939 70009 70109 70184 70259 70319 70389 70447 70505 70577 70699
## [1021] 70789 70879 70969 71059 71129 71157 71225 71313 71405 71497 71589 71681
## [1033] 71821 72076 72136 72236 72336 72441 72521 72721 72821 72921 73021 73121
## [1045] 73221head(cumsum(d2$Total))## [1] 318 723 1248 1873 2182 2587head(cumsum(d2$Attack))## [1] 49 111 193 293 345 409head(cumsum(d2$Defence))## [1] 49 112 195 318 361 419head(cumsum(d2$Sp_attack))## [1] 65 145 245 367 427 507head(cumsum(d2$Sp_defence))## [1] 65 145 245 365 415 480head(cumsum(d2$Speed))## [1] 45 105 185 265 330 410cummax(d2$HP)## [1] 45 60 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80
## [19] 80 80 80 80 83 83 83 83 83 83 83 83 83 83 83 83 83 83
## [37] 83 83 83 83 83 83 90 90 90 90 90 95 95 95 95 95 115 140
## [55] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
## [73] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
## [91] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
## [109] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
## [127] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140
## [145] 140 140 140 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
## [163] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
## [181] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
## [199] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
## [217] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
## [235] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
## [253] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
## [271] 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250
## [289] 250 250 250 250 250 250 255 255 255 255 255 255 255 255 255 255 255 255
## [307] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [325] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [343] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [361] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [379] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [397] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [415] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [433] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [451] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [469] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [487] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [505] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [523] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [541] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [559] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [577] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [595] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [613] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [631] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [649] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [667] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [685] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [703] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [721] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [739] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [757] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [775] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [793] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [811] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [829] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [847] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [865] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [883] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [901] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [919] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [937] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [955] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [973] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [991] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [1009] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [1027] 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
## [1045] 255head(cummax(d2$Total))## [1] 318 405 525 625 625 625head(cummax(d2$Attack))## [1] 49 62 82 100 100 100head(cummax(d2$Defence))## [1] 49 63 83 123 123 123head(cummax(d2$Sp_attack))## [1] 65 80 100 122 122 122head(cummax(d2$Sp_defence))## [1] 65 80 100 120 120 120head(cummax(d2$Speed))## [1] 45 60 80 80 80 80cummin(d2$HP)## [1] 45 45 45 45 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
## [25] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
## [49] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 10 10 10 10 10 10 10 10 10
## [73] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [97] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [121] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [145] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [169] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [193] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [217] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [241] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [265] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [289] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [313] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [337] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 1 1 1 1 1 1 1 1 1
## [361] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [385] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [409] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [433] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [457] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [481] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [505] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [529] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [553] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [577] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [601] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [625] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [649] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [673] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [697] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [721] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [745] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [769] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [793] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [817] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [841] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [865] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [913] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [937] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [961] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [985] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1009] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1033] 1 1 1 1 1 1 1 1 1 1 1 1 1cummin(d2$Attack)## [1] 49 49 49 49 49 49 49 49 49 48 48 48 48 30 20 20 20 20 20 20 20 20 20 20
## [25] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [49] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [73] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [97] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [121] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [145] 20 20 20 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [169] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [193] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [217] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [241] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [265] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [289] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [313] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [337] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [361] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [385] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [409] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [433] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [457] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [481] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [505] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [529] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [553] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [577] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [601] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [625] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [649] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [673] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [697] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [721] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [745] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [769] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [793] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [817] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [841] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [865] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [889] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [913] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [937] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [961] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [985] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1009] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1033] 5 5 5 5 5 5 5 5 5 5 5 5 5cummin(d2$Defence)## [1] 49 49 49 49 43 43 43 43 43 43 43 43 43 35 35 35 30 30 30 30 30 30 30 30
## [25] 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
## [49] 30 30 30 30 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
## [73] 20 20 20 20 20 20 20 20 20 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [97] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [121] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
## [145] 15 15 15 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [169] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [193] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [217] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [241] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [265] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [289] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [313] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [337] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [361] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [385] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [409] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [433] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [457] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [481] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [505] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [529] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [553] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [577] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [601] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [625] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [649] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [673] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [697] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [721] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [745] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [769] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [793] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [817] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [841] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [865] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [889] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [913] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [937] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [961] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [985] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1009] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [1033] 5 5 5 5 5 5 5 5 5 5 5 5 5cummin(d2$Sp_attack)## [1] 65 65 65 65 60 60 60 60 60 50 50 50 50 20 20 20 20 20 20 15 15 15 15 15
## [25] 15 15 15 15 15 15 15 15 15 15 15 15 15 10 10 10 10 10 10 10 10 10 10 10
## [49] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [73] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [97] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [121] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [145] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [169] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [193] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [217] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [241] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [265] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [289] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [313] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [337] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [361] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [385] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [409] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [433] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [457] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [481] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [505] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [529] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [553] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [577] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [601] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [625] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [649] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [673] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [697] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [721] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [745] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [769] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [793] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [817] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [841] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [865] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [889] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [913] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [937] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [961] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [985] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [1009] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [1033] 10 10 10 10 10 10 10 10 10 10 10 10 10cumprod(d2$HP)## [1] 4.500000e+01 2.700000e+03 2.160000e+05 1.728000e+07 6.739200e+08
## [6] 3.908736e+10 3.048814e+12 2.378075e+14 1.854898e+16 8.161553e+17
## [11] 4.815316e+19 3.804100e+21 3.005239e+23 1.352358e+25 6.761788e+26
## [16] 4.057073e+28 1.622829e+30 7.302731e+31 4.746775e+33 3.085404e+35
## [21] 1.234162e+37 7.775217e+38 6.453431e+40 5.356347e+42 1.606904e+44
## [26] 4.820713e+45 2.651392e+47 1.988544e+49 7.954176e+50 5.170214e+52
## [31] 1.809575e+54 1.085745e+56 3.800107e+57 1.710048e+59 1.026029e+61
## [36] 6.156174e+62 3.078087e+64 1.539044e+66 1.154283e+68 8.657120e+69
## [41] 4.761416e+71 3.332991e+73 2.999692e+75 1.379858e+77 8.417136e+78
## [46] 6.817880e+80 4.772516e+82 4.533890e+84 1.722878e+86 6.546937e+87
## [51] 4.779264e+89 3.488863e+91 4.012192e+93 5.617069e+95 2.246828e+97
## [56] 1.685121e+99 7.583044e+100 4.549826e+102 3.412370e+104 1.194329e+106
## [61] 7.165976e+107 4.299586e+109 3.009710e+111 3.009710e+112 3.009710e+113
## [66] 1.053399e+115 3.686895e+116 1.474758e+118 5.899032e+119 2.949516e+121
## [71] 1.917185e+123 1.246170e+125 6.230852e+126 4.984682e+128 1.993873e+130
## [76] 1.296017e+132 7.128095e+133 6.415285e+135 2.566114e+137 1.667974e+139
## [81] 1.501177e+141 3.752942e+142 1.501177e+144 8.256472e+145 4.541060e+147
## [86] 3.178742e+149 2.542993e+151 2.288694e+153 1.144347e+155 7.438256e+156
## [91] 5.950605e+158 2.380242e+160 1.904194e+162 7.616774e+163 3.046710e+165
## [96] 1.675690e+167 9.216297e+168 7.373037e+170 5.898430e+172 2.949215e+174
## [101] 1.474607e+176 9.584948e+177 6.230217e+179 5.607195e+181 5.046475e+183
## [106] 4.794152e+185 4.554444e+187 4.326722e+189 1.081680e+191 5.408402e+192
## [111] 2.812369e+194 1.462432e+196 5.118512e+197 3.071107e+199 1.996220e+201
## [116] 1.796598e+203 1.437278e+205 1.149823e+207 1.207314e+209 1.267679e+211
## [121] 3.803038e+212 1.901519e+214 5.704557e+215 2.567051e+217 1.540230e+219
## [126] 9.241382e+220 3.234484e+222 1.940690e+224 1.649587e+226 4.948760e+227
## [131] 2.721818e+229 1.088727e+231 6.532363e+232 3.919418e+234 3.723447e+236
## [136] 3.537275e+238 1.768637e+240 1.061182e+242 6.367095e+243 3.183547e+245
## [141] 1.591774e+247 1.432596e+249 5.730385e+250 3.724750e+252 2.421088e+254
## [146] 1.936870e+256 2.033714e+258 5.084284e+260 3.304785e+262 3.470024e+264
## [151] 3.643525e+266 1.093058e+268 6.011817e+269 2.705317e+271 2.164254e+273
## [156] 6.492762e+274 3.895657e+276 1.558263e+278 7.791314e+279 5.453920e+281
## [161] 3.545048e+283 2.304281e+285 1.497783e+287 9.735588e+288 6.328132e+290
## [166] 4.746099e+292 9.492198e+293 9.017589e+295 8.566709e+297 1.113672e+300
## [171] 5.345626e+301 2.940095e+303 1.911061e+305 2.484380e+307 Inf
## [176] Inf Inf Inf Inf Inf
## [181] Inf Inf Inf Inf Inf
## [186] Inf Inf Inf Inf Inf
## [191] Inf Inf Inf Inf Inf
## [196] Inf Inf Inf Inf Inf
## [201] Inf Inf Inf Inf Inf
## [206] Inf Inf Inf Inf Inf
## [211] Inf Inf Inf Inf Inf
## [216] Inf Inf Inf Inf Inf
## [221] Inf Inf Inf Inf Inf
## [226] Inf Inf Inf Inf Inf
## [231] Inf Inf Inf Inf Inf
## [236] Inf Inf Inf Inf Inf
## [241] Inf Inf Inf Inf Inf
## [246] Inf Inf Inf Inf Inf
## [251] Inf Inf Inf Inf Inf
## [256] Inf Inf Inf Inf Inf
## [261] Inf Inf Inf Inf Inf
## [266] Inf Inf Inf Inf Inf
## [271] Inf Inf Inf Inf Inf
## [276] Inf Inf Inf Inf Inf
## [281] Inf Inf Inf Inf Inf
## [286] Inf Inf Inf Inf Inf
## [291] Inf Inf Inf Inf Inf
## [296] Inf Inf Inf Inf Inf
## [301] Inf Inf Inf Inf Inf
## [306] Inf Inf Inf Inf Inf
## [311] Inf Inf Inf Inf Inf
## [316] Inf Inf Inf Inf Inf
## [321] Inf Inf Inf Inf Inf
## [326] Inf Inf Inf Inf Inf
## [331] Inf Inf Inf Inf Inf
## [336] Inf Inf Inf Inf Inf
## [341] Inf Inf Inf Inf Inf
## [346] Inf Inf Inf Inf Inf
## [351] Inf Inf Inf Inf Inf
## [356] Inf Inf Inf Inf Inf
## [361] Inf Inf Inf Inf Inf
## [366] Inf Inf Inf Inf Inf
## [371] Inf Inf Inf Inf Inf
## [376] Inf Inf Inf Inf Inf
## [381] Inf Inf Inf Inf Inf
## [386] Inf Inf Inf Inf Inf
## [391] Inf Inf Inf Inf Inf
## [396] Inf Inf Inf Inf Inf
## [401] Inf Inf Inf Inf Inf
## [406] Inf Inf Inf Inf Inf
## [411] Inf Inf Inf Inf Inf
## [416] Inf Inf Inf Inf Inf
## [421] Inf Inf Inf Inf Inf
## [426] Inf Inf Inf Inf Inf
## [431] Inf Inf Inf Inf Inf
## [436] Inf Inf Inf Inf Inf
## [441] Inf Inf Inf Inf Inf
## [446] Inf Inf Inf Inf Inf
## [451] Inf Inf Inf Inf Inf
## [456] Inf Inf Inf Inf Inf
## [461] Inf Inf Inf Inf Inf
## [466] Inf Inf Inf Inf Inf
## [471] Inf Inf Inf Inf Inf
## [476] Inf Inf Inf Inf Inf
## [481] Inf Inf Inf Inf Inf
## [486] Inf Inf Inf Inf Inf
## [491] Inf Inf Inf Inf Inf
## [496] Inf Inf Inf Inf Inf
## [501] Inf Inf Inf Inf Inf
## [506] Inf Inf Inf Inf Inf
## [511] Inf Inf Inf Inf Inf
## [516] Inf Inf Inf Inf Inf
## [521] Inf Inf Inf Inf Inf
## [526] Inf Inf Inf Inf Inf
## [531] Inf Inf Inf Inf Inf
## [536] Inf Inf Inf Inf Inf
## [541] Inf Inf Inf Inf Inf
## [546] Inf Inf Inf Inf Inf
## [551] Inf Inf Inf Inf Inf
## [556] Inf Inf Inf Inf Inf
## [561] Inf Inf Inf Inf Inf
## [566] Inf Inf Inf Inf Inf
## [571] Inf Inf Inf Inf Inf
## [576] Inf Inf Inf Inf Inf
## [581] Inf Inf Inf Inf Inf
## [586] Inf Inf Inf Inf Inf
## [591] Inf Inf Inf Inf Inf
## [596] Inf Inf Inf Inf Inf
## [601] Inf Inf Inf Inf Inf
## [606] Inf Inf Inf Inf Inf
## [611] Inf Inf Inf Inf Inf
## [616] Inf Inf Inf Inf Inf
## [621] Inf Inf Inf Inf Inf
## [626] Inf Inf Inf Inf Inf
## [631] Inf Inf Inf Inf Inf
## [636] Inf Inf Inf Inf Inf
## [641] Inf Inf Inf Inf Inf
## [646] Inf Inf Inf Inf Inf
## [651] Inf Inf Inf Inf Inf
## [656] Inf Inf Inf Inf Inf
## [661] Inf Inf Inf Inf Inf
## [666] Inf Inf Inf Inf Inf
## [671] Inf Inf Inf Inf Inf
## [676] Inf Inf Inf Inf Inf
## [681] Inf Inf Inf Inf Inf
## [686] Inf Inf Inf Inf Inf
## [691] Inf Inf Inf Inf Inf
## [696] Inf Inf Inf Inf Inf
## [701] Inf Inf Inf Inf Inf
## [706] Inf Inf Inf Inf Inf
## [711] Inf Inf Inf Inf Inf
## [716] Inf Inf Inf Inf Inf
## [721] Inf Inf Inf Inf Inf
## [726] Inf Inf Inf Inf Inf
## [731] Inf Inf Inf Inf Inf
## [736] Inf Inf Inf Inf Inf
## [741] Inf Inf Inf Inf Inf
## [746] Inf Inf Inf Inf Inf
## [751] Inf Inf Inf Inf Inf
## [756] Inf Inf Inf Inf Inf
## [761] Inf Inf Inf Inf Inf
## [766] Inf Inf Inf Inf Inf
## [771] Inf Inf Inf Inf Inf
## [776] Inf Inf Inf Inf Inf
## [781] Inf Inf Inf Inf Inf
## [786] Inf Inf Inf Inf Inf
## [791] Inf Inf Inf Inf Inf
## [796] Inf Inf Inf Inf Inf
## [801] Inf Inf Inf Inf Inf
## [806] Inf Inf Inf Inf Inf
## [811] Inf Inf Inf Inf Inf
## [816] Inf Inf Inf Inf Inf
## [821] Inf Inf Inf Inf Inf
## [826] Inf Inf Inf Inf Inf
## [831] Inf Inf Inf Inf Inf
## [836] Inf Inf Inf Inf Inf
## [841] Inf Inf Inf Inf Inf
## [846] Inf Inf Inf Inf Inf
## [851] Inf Inf Inf Inf Inf
## [856] Inf Inf Inf Inf Inf
## [861] Inf Inf Inf Inf Inf
## [866] Inf Inf Inf Inf Inf
## [871] Inf Inf Inf Inf Inf
## [876] Inf Inf Inf Inf Inf
## [881] Inf Inf Inf Inf Inf
## [886] Inf Inf Inf Inf Inf
## [891] Inf Inf Inf Inf Inf
## [896] Inf Inf Inf Inf Inf
## [901] Inf Inf Inf Inf Inf
## [906] Inf Inf Inf Inf Inf
## [911] Inf Inf Inf Inf Inf
## [916] Inf Inf Inf Inf Inf
## [921] Inf Inf Inf Inf Inf
## [926] Inf Inf Inf Inf Inf
## [931] Inf Inf Inf Inf Inf
## [936] Inf Inf Inf Inf Inf
## [941] Inf Inf Inf Inf Inf
## [946] Inf Inf Inf Inf Inf
## [951] Inf Inf Inf Inf Inf
## [956] Inf Inf Inf Inf Inf
## [961] Inf Inf Inf Inf Inf
## [966] Inf Inf Inf Inf Inf
## [971] Inf Inf Inf Inf Inf
## [976] Inf Inf Inf Inf Inf
## [981] Inf Inf Inf Inf Inf
## [986] Inf Inf Inf Inf Inf
## [991] Inf Inf Inf Inf Inf
## [996] Inf Inf Inf Inf Inf
## [1001] Inf Inf Inf Inf Inf
## [1006] Inf Inf Inf Inf Inf
## [1011] Inf Inf Inf Inf Inf
## [1016] Inf Inf Inf Inf Inf
## [1021] Inf Inf Inf Inf Inf
## [1026] Inf Inf Inf Inf Inf
## [1031] Inf Inf Inf Inf Inf
## [1036] Inf Inf Inf Inf Inf
## [1041] Inf Inf Inf Inf Infseq_along(d2)## [1] 1 2 3 4 5 6 7 8seq(from = 1, to = 5, by =2)## [1] 1 3 5cumsum(d2$HP) / seq_along(d2) ## Warning in cumsum(d2$HP)/seq_along(d2): longer object length is not a multiple
## of shorter object length## [1] 45.00000 52.50000 61.66667 66.25000 60.80000 60.33333
## [7] 62.85714 64.75000 596.00000 320.00000 233.00000 194.50000
## [13] 171.40000 150.33333 136.00000 126.50000 1052.00000 548.50000
## [19] 387.33333 306.75000 253.40000 221.66667 201.85714 187.00000
## [25] 1526.00000 778.00000 537.00000 421.50000 345.20000 298.50000
## [31] 260.85714 235.75000 1921.00000 983.00000 675.33333 521.50000
## [37] 427.20000 364.33333 323.00000 292.00000 2391.00000 1230.50000
## [43] 850.33333 649.25000 531.60000 456.50000 401.28571 363.00000
## [49] 2942.00000 1490.00000 1017.66667 781.50000 648.20000 563.50000
## [55] 488.71429 437.00000 3541.00000 1800.50000 1225.33333 927.75000
## [61] 754.20000 638.50000 557.28571 488.87500 3921.00000 1978.00000
## [67] 1330.33333 1007.75000 814.20000 686.83333 598.00000 531.37500
## [73] 4301.00000 2190.50000 1473.66667 1121.50000 908.20000 771.83333
## [79] 667.28571 592.00000 4826.00000 2425.50000 1630.33333 1236.50000
## [85] 1000.20000 845.16667 735.85714 655.12500 5291.00000 2678.00000
## [91] 1812.00000 1369.00000 1111.20000 932.66667 805.14286 711.37500
## [97] 5746.00000 2913.00000 1968.66667 1489.00000 1201.20000 1011.83333
## [103] 876.57143 778.25000 6316.00000 3205.50000 2168.66667 1650.25000
## [109] 1325.20000 1112.66667 961.14286 847.50000 6815.00000 3437.50000
## [115] 2313.33333 1757.50000 1422.00000 1198.33333 1042.14286 925.00000
## [121] 7430.00000 3740.00000 2503.33333 1888.75000 1523.00000 1279.16667
## [127] 1101.42857 971.25000 7855.00000 3942.50000 2646.66667 1995.00000
## [133] 1608.00000 1350.00000 1170.71429 1036.25000 8340.00000 4200.00000
## [139] 2820.00000 2127.50000 1712.00000 1441.66667 1241.42857 1094.37500
## [145] 8820.00000 4450.00000 3001.66667 2313.75000 1864.00000 1570.83333
## [151] 1361.42857 1195.00000 9615.00000 4830.00000 3246.66667 2442.50000
## [157] 1966.00000 1645.00000 1417.14286 1248.75000 10055.00000 5060.00000
## [163] 3395.00000 2562.50000 2063.00000 1731.66667 1487.14286 1313.12500
## [169] 10600.00000 5365.00000 3592.66667 2708.25000 2179.60000 1838.00000
## [175] 1584.71429 1394.75000 11223.00000 5629.00000 3776.00000 2839.50000
## [181] 2283.60000 1916.33333 1654.00000 1467.25000 11828.00000 5959.00000
## [187] 4002.66667 3024.50000 2437.60000 2046.33333 1759.85714 1547.50000
## [193] 12471.00000 6288.50000 4227.66667 3197.25000 2577.80000 2155.66667
## [199] 1856.28571 1634.25000 13113.00000 6585.50000 4416.33333 3324.75000
## [205] 2672.80000 2241.50000 1926.28571 1696.12500 13629.00000 6864.50000
## [211] 4589.66667 3456.00000 2772.80000 2322.33333 2002.71429 1761.75000
## [217] 14219.00000 7119.50000 4763.00000 3594.75000 2882.80000 2411.50000
## [223] 2072.71429 1821.75000 14629.00000 7349.50000 4929.66667 3719.75000
## [229] 2990.80000 2504.00000 2160.57143 1899.25000 15284.00000 7659.50000
## [235] 5124.66667 3862.25000 3100.80000 2589.00000 2229.85714 1959.25000
## [241] 15729.00000 7912.00000 5296.33333 3996.00000 3208.80000 2689.83333
## [247] 2319.14286 2036.75000 16342.00000 8266.00000 5534.00000 4163.00000
## [253] 3345.40000 2804.50000 2413.14286 2120.87500 17042.00000 8551.00000
## [259] 5730.66667 4314.25000 3465.40000 2899.50000 2488.14286 2187.12500
## [265] 17577.00000 8816.00000 5897.33333 4445.50000 3564.40000 2980.33333
## [271] 2561.71429 2254.00000 18097.00000 9078.50000 6064.00000 4566.75000
## [277] 3662.40000 3066.16667 2637.42857 2313.37500 18582.00000 9328.50000
## [283] 6244.00000 4705.50000 3782.40000 3166.16667 2724.28571 2390.62500
## [289] 19160.00000 9605.00000 6418.33333 4825.00000 3869.00000 3240.00000
## [295] 2813.57143 2473.12500 19900.00000 10000.00000 6683.33333 5030.00000
## [301] 4044.00000 3386.66667 2918.00000 2566.50000 20632.00000 10336.00000
## [307] 6907.33333 5198.00000 4172.40000 3484.50000 2995.28571 2630.87500
## [313] 21127.00000 10588.50000 7082.33333 5336.75000 4289.40000 3580.33333
## [319] 3078.85714 2698.75000 21628.00000 10853.00000 7261.33333 5457.25000
## [325] 4375.80000 3656.50000 3141.28571 2756.12500 22089.00000 11074.50000
## [331] 7409.66667 5567.25000 4467.80000 3738.16667 3209.85714 2816.12500
## [337] 22569.00000 11314.50000 7552.33333 5673.75000 4552.60000 3805.16667
## [343] 3267.28571 2867.62500 23001.00000 11530.50000 7707.00000 5800.25000
## [349] 4670.20000 3897.00000 3349.00000 2930.50000 23508.00000 11796.00000
## [355] 7898.66667 5942.00000 4782.40000 3993.66667 3427.42857 3005.25000
## [361] 24112.00000 12081.00000 8070.66667 6065.50000 4862.40000 4060.33333
## [367] 3488.85714 3061.50000 24562.00000 12296.00000 8217.33333 6178.00000
## [373] 4950.40000 4137.00000 3556.00000 3119.00000 25012.00000 12538.50000
## [379] 8380.66667 6298.00000 5052.40000 4227.00000 3629.57143 3184.62500
## [385] 25547.00000 12838.50000 8615.66667 6476.75000 5195.40000 4341.16667
## [391] 3731.00000 3272.12500 26257.00000 13158.50000 8787.33333 6603.00000
## [397] 5298.40000 4423.66667 3801.71429 3332.12500 26732.00000 13403.50000
## [403] 8960.00000 6738.25000 5408.60000 4522.16667 3883.28571 3411.62500
## [409] 27336.00000 13699.50000 9146.33333 6874.75000 5513.00000 4608.50000
## [415] 3956.57143 3471.37500 27791.00000 13943.00000 9318.66667 7006.50000
## [421] 5619.20000 4694.33333 4032.28571 3533.75000 28334.00000 14199.00000
## [427] 9472.66667 7114.50000 5711.40000 4772.00000 4099.57143 3595.25000
## [433] 28857.00000 14453.50000 9662.33333 7266.75000 5827.40000 4871.16667
## [439] 4191.00000 3671.50000 29427.00000 14741.00000 9860.66667 7406.25000
## [445] 5934.00000 4955.83333 4261.42857 3740.62500 29965.00000 15012.50000
## [451] 10035.00000 7546.25000 6053.00000 5057.50000 4346.42857 3813.12500
## [457] 30585.00000 15332.50000 10248.33333 7711.25000 6189.00000 5174.16667
## [463] 4449.28571 3906.25000 31355.00000 15727.50000 10501.66667 7888.75000
## [469] 6321.00000 5275.83333 4530.00000 3973.12500 31880.00000 15962.00000
## [475] 10662.66667 8016.00000 6423.40000 5363.50000 4609.28571 4038.12500
## [481] 32360.00000 16222.50000 10834.66667 8145.75000 6524.00000 5449.50000
## [487] 4677.42857 4100.25000 32882.00000 16461.00000 10994.00000 8262.25000
## [493] 6629.20000 5529.33333 4748.00000 4159.50000 33336.00000 16698.00000
## [499] 11152.00000 8381.50000 6711.20000 5604.33333 4812.28571 4217.62500
## [505] 33826.00000 16935.50000 11313.66667 8504.25000 6825.60000 5700.50000
## [511] 4899.00000 4305.37500 34498.00000 17281.50000 11542.66667 8672.00000
## [517] 6957.60000 5806.16667 4986.85714 4369.12500 35016.00000 17559.50000
## [523] 11725.33333 8810.75000 7058.60000 5885.50000 5059.00000 4436.12500
## [529] 35539.00000 17798.50000 11888.33333 8943.25000 7176.20000 6002.66667
## [535] 5150.85714 4515.75000 36196.00000 18132.00000 12124.00000 9103.00000
## [541] 7296.40000 6088.33333 5230.42857 4585.87500 36736.00000 18402.50000
## [547] 12283.33333 9227.50000 7400.00000 6181.66667 5308.57143 4653.75000
## [553] 37340.00000 18727.50000 12518.33333 9407.50000 7541.00000 6298.33333
## [559] 5410.85714 4742.62500 38006.00000 19040.50000 12730.33333 9569.00000
## [565] 7668.80000 6402.00000 5496.00000 4814.62500 38587.00000 19318.50000
## [571] 12895.66667 9684.25000 7757.40000 6472.83333 5555.28571 4870.25000
## [577] 39042.00000 19558.50000 13072.33333 9826.75000 7879.60000 6584.66667
## [583] 5665.42857 4976.00000 39928.00000 20004.00000 13369.33333 10044.50000
## [589] 8055.60000 6729.66667 5785.42857 5074.75000 40643.00000 20351.50000
## [595] 13592.66667 10210.75000 8186.60000 6840.50000 5871.14286 5146.62500
## [601] 41268.00000 20656.50000 13791.00000 10354.50000 8296.60000 6928.00000
## [607] 5944.14286 5209.12500 41723.00000 20899.00000 13949.33333 10480.75000
## [613] 8394.60000 7008.00000 6017.71429 5280.00000 42290.00000 21176.00000
## [619] 14144.00000 10619.25000 8510.40000 7101.16667 6096.71429 5345.25000
## [625] 42827.00000 21447.00000 14318.00000 10766.00000 8633.40000 7211.66667
## [631] 6192.14286 5428.75000 43535.00000 21792.50000 14553.33333 10941.25000
## [637] 8777.00000 7326.66667 6286.42857 5507.50000 44135.00000 22082.50000
## [643] 14735.00000 11066.25000 8861.00000 7394.16667 6344.28571 5560.00000
## [649] 44550.00000 22310.00000 14890.00000 11182.50000 8965.00000 7482.50000
## [655] 6423.57143 5633.75000 45175.00000 22640.00000 15128.33333 11365.00000
## [661] 9102.00000 7596.66667 6518.57143 5711.87500 45767.00000 22902.50000
## [667] 15281.00000 11475.25000 9191.00000 7671.50000 6583.42857 5769.87500
## [673] 46209.00000 23144.50000 15443.00000 11597.25000 9288.80000 7753.16667
## [679] 6652.00000 5828.00000 46694.00000 23369.50000 15601.33333 11728.50000
## [685] 9395.20000 7841.83333 6726.71429 5892.25000 47209.00000 23634.50000
## [691] 15783.00000 11851.00000 9490.80000 7920.66667 6799.00000 5963.37500
## [697] 47762.00000 23931.00000 16009.00000 12019.25000 9629.40000 8031.83333
## [703] 6895.00000 6038.12500 48365.00000 24212.50000 16153.33333 12131.25000
## [709] 9722.00000 8110.83333 6962.85714 6098.75000 48850.00000 24455.00000
## [715] 16318.66667 12255.50000 9819.60000 8192.16667 7035.42857 6166.00000
## [721] 49378.00000 24729.00000 16522.33333 12419.00000 9944.20000 8297.66667
## [727] 7123.28571 6240.25000 50011.00000 25028.00000 16707.00000 12554.00000
## [733] 10057.20000 8397.66667 7208.00000 6320.75000 50651.00000 25354.50000
## [739] 16920.33333 12708.25000 10185.00000 8496.66667 7295.00000 6394.50000
## [745] 51247.00000 25669.00000 17139.00000 12874.00000 10315.00000 8609.00000
## [751] 7393.42857 6481.75000 51943.00000 26016.00000 17385.66667 13070.50000
## [757] 10481.40000 8749.66667 7512.71429 6586.12500 52789.00000 26430.00000
## [763] 17638.66667 13244.25000 10613.00000 8850.83333 7594.85714 6654.87500
## [769] 53280.00000 26667.00000 17802.00000 13369.50000 10703.20000 8933.50000
## [775] 7663.71429 6713.50000 53786.00000 26912.00000 17956.33333 13487.25000
## [781] 10802.20000 9016.16667 7734.42857 6774.37500 54273.00000 27169.50000
## [787] 18154.00000 13632.25000 10924.80000 9116.50000 7823.00000 6854.37500
## [793] 54909.00000 27477.00000 18337.66667 13768.25000 11026.60000 9201.83333
## [799] 7901.71429 6921.75000 55456.00000 27754.50000 18531.66667 13909.25000
## [805] 11141.80000 9293.16667 7974.85714 6984.25000 55945.00000 27994.50000
## [811] 18683.66667 14027.25000 11238.20000 9378.00000 8055.85714 7060.75000
## [817] 56564.00000 28315.50000 18893.66667 14181.50000 11358.80000 9480.66667
## [823] 8134.42857 7123.00000 57069.00000 28559.00000 19054.00000 14304.00000
## [829] 11455.00000 9556.66667 8199.28571 7183.75000 57555.00000 28805.00000
## [835] 19235.00000 14436.25000 11566.00000 9659.33333 8297.42857 7273.75000
## [841] 58244.00000 29230.00000 19503.33333 14640.00000 11728.00000 9786.66667
## [847] 8400.00000 7358.50000 58946.00000 29512.00000 19689.66667 14783.50000
## [853] 11845.80000 9879.83333 8477.00000 7427.37500 59454.00000 29754.50000
## [859] 19863.00000 14909.25000 11945.00000 9962.00000 8547.00000 7488.25000
## [865] 59953.00000 30025.00000 20041.66667 15050.00000 12055.00000 10058.33333
## [871] 8627.14286 7556.25000 60495.00000 30270.00000 20205.00000 15175.00000
## [877] 12155.00000 10136.66667 8695.00000 7614.37500 60965.00000 30517.50000
## [883] 20378.33333 15293.25000 12248.20000 10213.50000 8764.42857 7673.87500
## [889] 61451.00000 30749.50000 20522.33333 15409.25000 12351.40000 10299.83333
## [895] 8835.85714 7740.37500 61974.00000 31032.00000 20721.33333 15547.25000
## [901] 12452.80000 10386.50000 8914.85714 7807.37500 62554.00000 31324.50000
## [907] 20903.00000 15692.25000 12566.80000 10482.33333 8994.14286 7876.75000
## [913] 63082.00000 31580.00000 21076.66667 15818.75000 12666.00000 10567.50000
## [919] 9067.85714 7943.12500 63615.00000 31842.50000 21242.66667 15942.75000
## [925] 12781.60000 10674.16667 9164.85714 8032.62500 64332.00000 32207.50000
## [931] 21504.00000 16142.75000 12958.80000 10815.16667 9284.00000 8135.62500
## [937] 65182.00000 32631.00000 21784.00000 16354.75000 13098.40000 10925.50000
## [943] 9372.28571 8211.75000 65740.00000 32937.50000 21975.00000 16498.75000
## [949] 13219.00000 11024.16667 9458.57143 8286.25000 66340.00000 33202.50000
## [955] 22158.33333 16636.25000 13333.00000 11117.16667 9538.71429 8358.62500
## [961] 66894.00000 33472.00000 22334.66667 16761.00000 13422.80000 11192.33333
## [967] 9602.00000 8407.00000 67328.00000 33689.00000 22489.33333 16881.75000
## [973] 13519.20000 11271.00000 9672.28571 8477.00000 67856.00000 33963.00000
## [979] 22678.66667 17022.00000 13632.00000 11371.66667 9753.00000 8541.50000
## [985] 68372.00000 34223.50000 22840.66667 17143.00000 13734.40000 11453.66667
## [991] 9828.85714 8605.25000 68902.00000 34472.00000 23000.33333 17264.50000
## [997] 13820.60000 11528.00000 9894.71429 8669.50000 69426.00000 34743.00000
## [1003] 23182.66667 17407.00000 13937.20000 11621.83333 9970.85714 8732.62500
## [1009] 69909.00000 34969.50000 23336.33333 17527.25000 14036.80000 11709.83333
## [1015] 10045.57143 8798.62500 70447.00000 35252.50000 23525.66667 17674.75000
## [1021] 14157.80000 11813.16667 10138.42857 8882.37500 71129.00000 35578.50000
## [1027] 23741.66667 17828.25000 14281.00000 11916.16667 10227.00000 8960.12500
## [1033] 71821.00000 36038.00000 24045.33333 18059.00000 14467.20000 12073.50000
## [1039] 10360.14286 9090.12500 72821.00000 36460.50000 24340.33333 18280.25000
## [1045] 14644.20000md = seq_along(d2)
md## [1] 1 2 3 4 5 6 7 8mean(d2[,2])## [1] 439.3148mean(d2[2,])## Warning in mean.default(d2[2, ]): argument is not numeric or logical: returning
## NA## [1] NAtable(d2$HP)##
## 1 10 20 25 28 30 31 35 36 37 38 39 40 41 42 43 44 45 46 47
## 1 2 6 4 2 16 1 17 1 1 11 2 48 4 4 5 7 47 3 2
## 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67
## 6 3 76 2 5 3 5 41 1 5 9 8 83 6 8 3 6 58 3 6
## 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 88
## 13 3 77 5 10 6 5 58 5 4 13 7 54 1 2 4 2 22 4 4
## 89 90 91 92 93 95 97 98 99 100 101 103 104 105 106 107 108 109 110 111
## 3 40 7 5 1 29 7 1 1 44 1 3 1 14 5 1 4 3 11 1
## 114 115 116 120 122 123 125 126 130 135 137 140 144 150 160 165 170 190 200 216
## 1 3 1 5 1 2 4 2 3 2 2 2 1 4 1 1 1 1 1 1
## 223 250 255
## 1 1 2d.mat = as.matrix(d2)
d.mat## Name Total HP Attack Defence Sp_attack Sp_defence
## [1,] "Bulbasaur" " 318" " 45" " 49" " 49" " 65" " 65"
## [2,] "Ivysaur" " 405" " 60" " 62" " 63" " 80" " 80"
## [3,] "Venusaur" " 525" " 80" " 82" " 83" "100" "100"
## [4,] "Mega Venusaur" " 625" " 80" "100" "123" "122" "120"
## [5,] "Charmander" " 309" " 39" " 52" " 43" " 60" " 50"
## [6,] "Charmeleon" " 405" " 58" " 64" " 58" " 80" " 65"
## [7,] "Charizard" " 534" " 78" " 84" " 78" "109" " 85"
## [8,] "Mega Charizard" " 634" " 78" "130" "111" "130" " 85"
## [9,] "Mega Charizard X" " 634" " 78" "104" " 78" "159" "115"
## [10,] "Squirtle" " 314" " 44" " 48" " 65" " 50" " 64"
## [11,] "Wartortle" " 405" " 59" " 63" " 80" " 65" " 80"
## [12,] "Blastoise" " 530" " 79" " 83" "100" " 85" "105"
## [13,] "Mega Blastoise" " 630" " 79" "103" "120" "135" "115"
## [14,] "Caterpie" " 195" " 45" " 30" " 35" " 20" " 20"
## [15,] "Metapod" " 205" " 50" " 20" " 55" " 25" " 25"
## [16,] "Butterfree" " 395" " 60" " 45" " 50" " 90" " 80"
## [17,] "Weedle" " 195" " 40" " 35" " 30" " 20" " 20"
## [18,] "Kakuna" " 205" " 45" " 25" " 50" " 25" " 25"
## [19,] "Beedrill" " 395" " 65" " 90" " 40" " 45" " 80"
## [20,] "Mega Beedrill" " 495" " 65" "150" " 40" " 15" " 80"
## [21,] "Pidgey" " 251" " 40" " 45" " 40" " 35" " 35"
## [22,] "Pidgeotto" " 349" " 63" " 60" " 55" " 50" " 50"
## [23,] "Pidgeot" " 479" " 83" " 80" " 75" " 70" " 70"
## [24,] "Mega Pidgeot" " 579" " 83" " 80" " 80" "135" " 80"
## [25,] "Rattata" " 253" " 30" " 56" " 35" " 25" " 35"
## [26,] "Mega Rattata" " 253" " 30" " 56" " 35" " 25" " 35"
## [27,] "Raticate" " 413" " 55" " 81" " 60" " 50" " 70"
## [28,] "Mega Raticate" " 413" " 75" " 71" " 70" " 40" " 80"
## [29,] "Spearow" " 262" " 40" " 60" " 30" " 31" " 31"
## [30,] "Fearow" " 442" " 65" " 90" " 65" " 61" " 61"
## [31,] "Ekans" " 288" " 35" " 60" " 44" " 40" " 54"
## [32,] "Arbok" " 448" " 60" " 95" " 69" " 65" " 79"
## [33,] "Pikachu" " 320" " 35" " 55" " 40" " 50" " 50"
## [34,] "Mega Pikachu" " 430" " 45" " 80" " 50" " 75" " 60"
## [35,] "Raichu" " 485" " 60" " 90" " 55" " 90" " 80"
## [36,] "Mega Raichu" " 485" " 60" " 85" " 50" " 95" " 85"
## [37,] "Sandshrew" " 300" " 50" " 75" " 85" " 20" " 30"
## [38,] "Mega Sandshrew" " 300" " 50" " 75" " 90" " 10" " 35"
## [39,] "Sandslash" " 450" " 75" "100" "110" " 45" " 55"
## [40,] "Mega Sandslash" " 450" " 75" "100" "120" " 25" " 65"
## [41,] "Nidoranâ\231\200" " 275" " 55" " 47" " 52" " 40" " 40"
## [42,] "Nidorina" " 365" " 70" " 62" " 67" " 55" " 55"
## [43,] "Nidoqueen" " 505" " 90" " 92" " 87" " 75" " 85"
## [44,] "Nidoranâ\231‚" " 273" " 46" " 57" " 40" " 40" " 40"
## [45,] "Nidorino" " 365" " 61" " 72" " 57" " 55" " 55"
## [46,] "Nidoking" " 505" " 81" "102" " 77" " 85" " 75"
## [47,] "Clefairy" " 323" " 70" " 45" " 48" " 60" " 65"
## [48,] "Clefable" " 483" " 95" " 70" " 73" " 95" " 90"
## [49,] "Vulpix" " 299" " 38" " 41" " 40" " 50" " 65"
## [50,] "Mega Vulpix" " 299" " 38" " 41" " 40" " 50" " 65"
## [51,] "Ninetales" " 505" " 73" " 76" " 75" " 81" "100"
## [52,] "Mega Ninetales" " 505" " 73" " 67" " 75" " 81" "100"
## [53,] "Jigglypuff" " 270" "115" " 45" " 20" " 45" " 25"
## [54,] "Wigglytuff" " 435" "140" " 70" " 45" " 85" " 50"
## [55,] "Zubat" " 245" " 40" " 45" " 35" " 30" " 40"
## [56,] "Golbat" " 455" " 75" " 80" " 70" " 65" " 75"
## [57,] "Oddish" " 320" " 45" " 50" " 55" " 75" " 65"
## [58,] "Gloom" " 395" " 60" " 65" " 70" " 85" " 75"
## [59,] "Vileplume" " 490" " 75" " 80" " 85" "110" " 90"
## [60,] "Paras" " 285" " 35" " 70" " 55" " 45" " 55"
## [61,] "Parasect" " 405" " 60" " 95" " 80" " 60" " 80"
## [62,] "Venonat" " 305" " 60" " 55" " 50" " 40" " 55"
## [63,] "Venomoth" " 450" " 70" " 65" " 60" " 90" " 75"
## [64,] "Diglett" " 265" " 10" " 55" " 25" " 35" " 45"
## [65,] "Mega Diglett" " 265" " 10" " 55" " 30" " 35" " 45"
## [66,] "Dugtrio" " 425" " 35" "100" " 50" " 50" " 70"
## [67,] "Mega Dugtrio" " 425" " 35" "100" " 60" " 50" " 70"
## [68,] "Meowth" " 290" " 40" " 45" " 35" " 40" " 40"
## [69,] "Mega Meowth" " 290" " 40" " 35" " 35" " 50" " 40"
## [70,] "Mega Meowth X" " 290" " 50" " 65" " 55" " 40" " 40"
## [71,] "Persian" " 440" " 65" " 70" " 60" " 65" " 65"
## [72,] "Mega Persian" " 440" " 65" " 60" " 60" " 75" " 65"
## [73,] "Psyduck" " 320" " 50" " 52" " 48" " 65" " 50"
## [74,] "Golduck" " 500" " 80" " 82" " 78" " 95" " 80"
## [75,] "Mankey" " 305" " 40" " 80" " 35" " 35" " 45"
## [76,] "Primeape" " 455" " 65" "105" " 60" " 60" " 70"
## [77,] "Growlithe" " 350" " 55" " 70" " 45" " 70" " 50"
## [78,] "Arcanine" " 555" " 90" "110" " 80" "100" " 80"
## [79,] "Poliwag" " 300" " 40" " 50" " 40" " 40" " 40"
## [80,] "Poliwhirl" " 385" " 65" " 65" " 65" " 50" " 50"
## [81,] "Poliwrath" " 510" " 90" " 95" " 95" " 70" " 90"
## [82,] "Abra" " 310" " 25" " 20" " 15" "105" " 55"
## [83,] "Kadabra" " 400" " 40" " 35" " 30" "120" " 70"
## [84,] "Alakazam" " 500" " 55" " 50" " 45" "135" " 95"
## [85,] "Mega Alakazam" " 600" " 55" " 50" " 65" "175" "105"
## [86,] "Machop" " 305" " 70" " 80" " 50" " 35" " 35"
## [87,] "Machoke" " 405" " 80" "100" " 70" " 50" " 60"
## [88,] "Machamp" " 505" " 90" "130" " 80" " 65" " 85"
## [89,] "Bellsprout" " 300" " 50" " 75" " 35" " 70" " 30"
## [90,] "Weepinbell" " 390" " 65" " 90" " 50" " 85" " 45"
## [91,] "Victreebel" " 490" " 80" "105" " 65" "100" " 70"
## [92,] "Tentacool" " 335" " 40" " 40" " 35" " 50" "100"
## [93,] "Tentacruel" " 515" " 80" " 70" " 65" " 80" "120"
## [94,] "Geodude" " 300" " 40" " 80" "100" " 30" " 30"
## [95,] "Mega Geodude" " 300" " 40" " 80" "100" " 30" " 30"
## [96,] "Graveler" " 390" " 55" " 95" "115" " 45" " 45"
## [97,] "Mega Graveler" " 390" " 55" " 95" "115" " 45" " 45"
## [98,] "Golem" " 495" " 80" "120" "130" " 55" " 65"
## [99,] "Mega Golem" " 495" " 80" "120" "130" " 55" " 65"
## [100,] "Ponyta" " 410" " 50" " 85" " 55" " 65" " 65"
## [101,] "Mega Ponyta" " 410" " 50" " 85" " 55" " 65" " 65"
## [102,] "Rapidash" " 500" " 65" "100" " 70" " 80" " 80"
## [103,] "Mega Rapidash" " 500" " 65" "100" " 70" " 80" " 80"
## [104,] "Slowpoke" " 315" " 90" " 65" " 65" " 40" " 40"
## [105,] "Mega Slowpoke" " 315" " 90" " 65" " 65" " 40" " 40"
## [106,] "Slowbro" " 490" " 95" " 75" "110" "100" " 80"
## [107,] "Mega Slowbro" " 590" " 95" " 75" "180" "130" " 80"
## [108,] "Mega Slowbro X" " 490" " 95" "100" " 95" "100" " 70"
## [109,] "Magnemite" " 325" " 25" " 35" " 70" " 95" " 55"
## [110,] "Magneton" " 465" " 50" " 60" " 95" "120" " 70"
## [111,] "Farfetch'd" " 377" " 52" " 90" " 55" " 58" " 62"
## [112,] "Mega Farfetch'd" " 377" " 52" " 95" " 55" " 58" " 62"
## [113,] "Doduo" " 310" " 35" " 85" " 45" " 35" " 35"
## [114,] "Dodrio" " 470" " 60" "110" " 70" " 60" " 60"
## [115,] "Seel" " 325" " 65" " 45" " 55" " 45" " 70"
## [116,] "Dewgong" " 475" " 90" " 70" " 80" " 70" " 95"
## [117,] "Grimer" " 325" " 80" " 80" " 50" " 40" " 50"
## [118,] "Mega Grimer" " 325" " 80" " 80" " 50" " 40" " 50"
## [119,] "Muk" " 500" "105" "105" " 75" " 65" "100"
## [120,] "Mega Muk" " 500" "105" "105" " 75" " 65" "100"
## [121,] "Shellder" " 305" " 30" " 65" "100" " 45" " 25"
## [122,] "Cloyster" " 525" " 50" " 95" "180" " 85" " 45"
## [123,] "Gastly" " 310" " 30" " 35" " 30" "100" " 35"
## [124,] "Haunter" " 405" " 45" " 50" " 45" "115" " 55"
## [125,] "Gengar" " 500" " 60" " 65" " 60" "130" " 75"
## [126,] "Mega Gengar" " 600" " 60" " 65" " 80" "170" " 95"
## [127,] "Onix" " 385" " 35" " 45" "160" " 30" " 45"
## [128,] "Drowzee" " 328" " 60" " 48" " 45" " 43" " 90"
## [129,] "Hypno" " 483" " 85" " 73" " 70" " 73" "115"
## [130,] "Krabby" " 325" " 30" "105" " 90" " 25" " 25"
## [131,] "Kingler" " 475" " 55" "130" "115" " 50" " 50"
## [132,] "Voltorb" " 330" " 40" " 30" " 50" " 55" " 55"
## [133,] "Electrode" " 490" " 60" " 50" " 70" " 80" " 80"
## [134,] "Exeggcute" " 325" " 60" " 40" " 80" " 60" " 45"
## [135,] "Exeggutor" " 530" " 95" " 95" " 85" "125" " 75"
## [136,] "Mega Exeggutor" " 530" " 95" "105" " 85" "125" " 75"
## [137,] "Cubone" " 320" " 50" " 50" " 95" " 40" " 50"
## [138,] "Marowak" " 425" " 60" " 80" "110" " 50" " 80"
## [139,] "Mega Marowak" " 425" " 60" " 80" "110" " 50" " 80"
## [140,] "Hitmonlee" " 455" " 50" "120" " 53" " 35" "110"
## [141,] "Hitmonchan" " 455" " 50" "105" " 79" " 35" "110"
## [142,] "Lickitung" " 385" " 90" " 55" " 75" " 60" " 75"
## [143,] "Koffing" " 340" " 40" " 65" " 95" " 60" " 45"
## [144,] "Weezing" " 490" " 65" " 90" "120" " 85" " 70"
## [145,] "Mega Weezing" " 490" " 65" " 90" "120" " 85" " 70"
## [146,] "Rhyhorn" " 345" " 80" " 85" " 95" " 30" " 30"
## [147,] "Rhydon" " 485" "105" "130" "120" " 45" " 45"
## [148,] "Chansey" " 450" "250" " 5" " 5" " 35" "105"
## [149,] "Tangela" " 435" " 65" " 55" "115" "100" " 40"
## [150,] "Kangaskhan" " 490" "105" " 95" " 80" " 40" " 80"
## [151,] "Mega Kangaskhan" " 590" "105" "125" "100" " 60" "100"
## [152,] "Horsea" " 295" " 30" " 40" " 70" " 70" " 25"
## [153,] "Seadra" " 440" " 55" " 65" " 95" " 95" " 45"
## [154,] "Goldeen" " 320" " 45" " 67" " 60" " 35" " 50"
## [155,] "Seaking" " 450" " 80" " 92" " 65" " 65" " 80"
## [156,] "Staryu" " 340" " 30" " 45" " 55" " 70" " 55"
## [157,] "Starmie" " 520" " 60" " 75" " 85" "100" " 85"
## [158,] "Mr. Mime" " 460" " 40" " 45" " 65" "100" "120"
## [159,] "Mega Mr. Mime" " 460" " 50" " 65" " 65" " 90" " 90"
## [160,] "Scyther" " 500" " 70" "110" " 80" " 55" " 80"
## [161,] "Jynx" " 455" " 65" " 50" " 35" "115" " 95"
## [162,] "Electabuzz" " 490" " 65" " 83" " 57" " 95" " 85"
## [163,] "Magmar" " 495" " 65" " 95" " 57" "100" " 85"
## [164,] "Pinsir" " 500" " 65" "125" "100" " 55" " 70"
## [165,] "Mega Pinsir" " 600" " 65" "155" "120" " 65" " 90"
## [166,] "Tauros" " 490" " 75" "100" " 95" " 40" " 70"
## [167,] "Magikarp" " 200" " 20" " 10" " 55" " 15" " 20"
## [168,] "Gyarados" " 540" " 95" "125" " 79" " 60" "100"
## [169,] "Mega Gyarados" " 640" " 95" "155" "109" " 70" "130"
## [170,] "Lapras" " 535" "130" " 85" " 80" " 85" " 95"
## [171,] "Ditto" " 288" " 48" " 48" " 48" " 48" " 48"
## [172,] "Eevee" " 325" " 55" " 55" " 50" " 45" " 65"
## [173,] "Mega Eevee" " 435" " 65" " 75" " 70" " 65" " 85"
## [174,] "Vaporeon" " 525" "130" " 65" " 60" "110" " 95"
## [175,] "Jolteon" " 525" " 65" " 65" " 60" "110" " 95"
## [176,] "Flareon" " 525" " 65" "130" " 60" " 95" "110"
## [177,] "Porygon" " 395" " 65" " 60" " 70" " 85" " 75"
## [178,] "Omanyte" " 355" " 35" " 40" "100" " 90" " 55"
## [179,] "Omastar" " 495" " 70" " 60" "125" "115" " 70"
## [180,] "Kabuto" " 355" " 30" " 80" " 90" " 55" " 45"
## [181,] "Kabutops" " 495" " 60" "115" "105" " 65" " 70"
## [182,] "Aerodactyl" " 515" " 80" "105" " 65" " 60" " 75"
## [183,] "Mega Aerodactyl" " 615" " 80" "135" " 85" " 70" " 95"
## [184,] "Snorlax" " 540" "160" "110" " 65" " 65" "110"
## [185,] "Articuno" " 580" " 90" " 85" "100" " 95" "125"
## [186,] "Mega Articuno" " 580" " 90" " 85" " 85" "125" "100"
## [187,] "Zapdos" " 580" " 90" " 90" " 85" "125" " 90"
## [188,] "Mega Zapdos" " 580" " 90" "125" " 90" " 85" " 90"
## [189,] "Moltres" " 580" " 90" "100" " 90" "125" " 85"
## [190,] "Mega Moltres" " 580" " 90" " 85" " 90" "100" "125"
## [191,] "Dratini" " 300" " 41" " 64" " 45" " 50" " 50"
## [192,] "Dragonair" " 420" " 61" " 84" " 65" " 70" " 70"
## [193,] "Dragonite" " 600" " 91" "134" " 95" "100" "100"
## [194,] "Mewtwo" " 680" "106" "110" " 90" "154" " 90"
## [195,] "Mega Mewtwo" " 780" "106" "190" "100" "154" "100"
## [196,] "Mega Mewtwo X" " 780" "106" "150" " 70" "194" "120"
## [197,] "Mew" " 600" "100" "100" "100" "100" "100"
## [198,] "Chikorita" " 318" " 45" " 49" " 65" " 49" " 65"
## [199,] "Bayleef" " 405" " 60" " 62" " 80" " 63" " 80"
## [200,] "Meganium" " 525" " 80" " 82" "100" " 83" "100"
## [201,] "Cyndaquil" " 309" " 39" " 52" " 43" " 60" " 50"
## [202,] "Quilava" " 405" " 58" " 64" " 58" " 80" " 65"
## [203,] "Typhlosion" " 534" " 78" " 84" " 78" "109" " 85"
## [204,] "Totodile" " 314" " 50" " 65" " 64" " 44" " 48"
## [205,] "Croconaw" " 405" " 65" " 80" " 80" " 59" " 63"
## [206,] "Feraligatr" " 530" " 85" "105" "100" " 79" " 83"
## [207,] "Sentret" " 215" " 35" " 46" " 34" " 35" " 45"
## [208,] "Furret" " 415" " 85" " 76" " 64" " 45" " 55"
## [209,] "Hoothoot" " 262" " 60" " 30" " 30" " 36" " 56"
## [210,] "Noctowl" " 452" "100" " 50" " 50" " 86" " 96"
## [211,] "Ledyba" " 265" " 40" " 20" " 30" " 40" " 80"
## [212,] "Ledian" " 390" " 55" " 35" " 50" " 55" "110"
## [213,] "Spinarak" " 250" " 40" " 60" " 40" " 40" " 40"
## [214,] "Ariados" " 400" " 70" " 90" " 70" " 60" " 70"
## [215,] "Crobat" " 535" " 85" " 90" " 80" " 70" " 80"
## [216,] "Chinchou" " 330" " 75" " 38" " 38" " 56" " 56"
## [217,] "Lanturn" " 460" "125" " 58" " 58" " 76" " 76"
## [218,] "Pichu" " 205" " 20" " 40" " 15" " 35" " 35"
## [219,] "Cleffa" " 218" " 50" " 25" " 28" " 45" " 55"
## [220,] "Igglybuff" " 210" " 90" " 30" " 15" " 40" " 20"
## [221,] "Togepi" " 245" " 35" " 20" " 65" " 40" " 65"
## [222,] "Togetic" " 405" " 55" " 40" " 85" " 80" "105"
## [223,] "Natu" " 320" " 40" " 50" " 45" " 70" " 45"
## [224,] "Xatu" " 470" " 65" " 75" " 70" " 95" " 70"
## [225,] "Mareep" " 280" " 55" " 40" " 40" " 65" " 45"
## [226,] "Flaaffy" " 365" " 70" " 55" " 55" " 80" " 60"
## [227,] "Ampharos" " 510" " 90" " 75" " 85" "115" " 90"
## [228,] "Mega Ampharos" " 610" " 90" " 95" "105" "165" "110"
## [229,] "Bellossom" " 490" " 75" " 80" " 95" " 90" "100"
## [230,] "Marill" " 250" " 70" " 20" " 50" " 20" " 50"
## [231,] "Azumarill" " 420" "100" " 50" " 80" " 60" " 80"
## [232,] "Sudowoodo" " 410" " 70" "100" "115" " 30" " 65"
## [233,] "Politoed" " 500" " 90" " 75" " 75" " 90" "100"
## [234,] "Hoppip" " 250" " 35" " 35" " 40" " 35" " 55"
## [235,] "Skiploom" " 340" " 55" " 45" " 50" " 45" " 65"
## [236,] "Jumpluff" " 460" " 75" " 55" " 70" " 55" " 95"
## [237,] "Aipom" " 360" " 55" " 70" " 55" " 40" " 55"
## [238,] "Sunkern" " 180" " 30" " 30" " 30" " 30" " 30"
## [239,] "Sunflora" " 425" " 75" " 75" " 55" "105" " 85"
## [240,] "Yanma" " 390" " 65" " 65" " 45" " 75" " 45"
## [241,] "Wooper" " 210" " 55" " 45" " 45" " 25" " 25"
## [242,] "Quagsire" " 430" " 95" " 85" " 85" " 65" " 65"
## [243,] "Espeon" " 525" " 65" " 65" " 60" "130" " 95"
## [244,] "Umbreon" " 525" " 95" " 65" "110" " 60" "130"
## [245,] "Murkrow" " 405" " 60" " 85" " 42" " 85" " 42"
## [246,] "Slowking" " 490" " 95" " 75" " 80" "100" "110"
## [247,] "Mega Slowking" " 490" " 95" " 65" " 80" "110" "110"
## [248,] "Misdreavus" " 435" " 60" " 60" " 60" " 85" " 85"
## [249,] "Unown" " 336" " 48" " 72" " 48" " 72" " 48"
## [250,] "Wobbuffet" " 405" "190" " 33" " 58" " 33" " 58"
## [251,] "Girafarig" " 455" " 70" " 80" " 65" " 90" " 65"
## [252,] "Pineco" " 290" " 50" " 65" " 90" " 35" " 35"
## [253,] "Forretress" " 465" " 75" " 90" "140" " 60" " 60"
## [254,] "Dunsparce" " 415" "100" " 70" " 70" " 65" " 65"
## [255,] "Gligar" " 430" " 65" " 75" "105" " 35" " 65"
## [256,] "Steelix" " 510" " 75" " 85" "200" " 55" " 65"
## [257,] "Mega Steelix" " 610" " 75" "125" "230" " 55" " 95"
## [258,] "Snubbull" " 300" " 60" " 80" " 50" " 40" " 40"
## [259,] "Granbull" " 450" " 90" "120" " 75" " 60" " 60"
## [260,] "Qwilfish" " 440" " 65" " 95" " 85" " 55" " 55"
## [261,] "Scizor" " 500" " 70" "130" "100" " 55" " 80"
## [262,] "Mega Scizor" " 600" " 70" "150" "140" " 65" "100"
## [263,] "Shuckle" " 505" " 20" " 10" "230" " 10" "230"
## [264,] "Heracross" " 500" " 80" "125" " 75" " 40" " 95"
## [265,] "Mega Heracross" " 600" " 80" "185" "115" " 40" "105"
## [266,] "Sneasel" " 430" " 55" " 95" " 55" " 35" " 75"
## [267,] "Teddiursa" " 330" " 60" " 80" " 50" " 50" " 50"
## [268,] "Ursaring" " 500" " 90" "130" " 75" " 75" " 75"
## [269,] "Slugma" " 250" " 40" " 40" " 40" " 70" " 40"
## [270,] "Magcargo" " 430" " 60" " 50" "120" " 90" " 80"
## [271,] "Swinub" " 250" " 50" " 50" " 40" " 30" " 30"
## [272,] "Piloswine" " 450" "100" "100" " 80" " 60" " 60"
## [273,] "Corsola" " 410" " 65" " 55" " 95" " 65" " 95"
## [274,] "Mega Corsola" " 410" " 60" " 55" "100" " 65" "100"
## [275,] "Remoraid" " 300" " 35" " 65" " 35" " 65" " 35"
## [276,] "Octillery" " 480" " 75" "105" " 75" "105" " 75"
## [277,] "Delibird" " 330" " 45" " 55" " 45" " 65" " 45"
## [278,] "Mantine" " 485" " 85" " 40" " 70" " 80" "140"
## [279,] "Skarmory" " 465" " 65" " 80" "140" " 40" " 70"
## [280,] "Houndour" " 330" " 45" " 60" " 30" " 80" " 50"
## [281,] "Houndoom" " 500" " 75" " 90" " 50" "110" " 80"
## [282,] "Mega Houndoom" " 600" " 75" " 90" " 90" "140" " 90"
## [283,] "Kingdra" " 540" " 75" " 95" " 95" " 95" " 95"
## [284,] "Phanpy" " 330" " 90" " 60" " 60" " 40" " 40"
## [285,] "Donphan" " 500" " 90" "120" "120" " 60" " 60"
## [286,] "Porygon2" " 515" " 85" " 80" " 90" "105" " 95"
## [287,] "Stantler" " 465" " 73" " 95" " 62" " 85" " 65"
## [288,] "Smeargle" " 250" " 55" " 20" " 35" " 20" " 45"
## [289,] "Tyrogue" " 210" " 35" " 35" " 35" " 35" " 35"
## [290,] "Hitmontop" " 455" " 50" " 95" " 95" " 35" "110"
## [291,] "Smoochum" " 305" " 45" " 30" " 15" " 85" " 65"
## [292,] "Elekid" " 360" " 45" " 63" " 37" " 65" " 55"
## [293,] "Magby" " 365" " 45" " 75" " 37" " 70" " 55"
## [294,] "Miltank" " 490" " 95" " 80" "105" " 40" " 70"
## [295,] "Blissey" " 540" "255" " 10" " 10" " 75" "135"
## [296,] "Raikou" " 580" " 90" " 85" " 75" "115" "100"
## [297,] "Entei" " 580" "115" "115" " 85" " 90" " 75"
## [298,] "Suicune" " 580" "100" " 75" "115" " 90" "115"
## [299,] "Larvitar" " 300" " 50" " 64" " 50" " 45" " 50"
## [300,] "Pupitar" " 410" " 70" " 84" " 70" " 65" " 70"
## [301,] "Tyranitar" " 600" "100" "134" "110" " 95" "100"
## [302,] "Mega Tyranitar" " 700" "100" "164" "150" " 95" "120"
## [303,] "Lugia" " 680" "106" " 90" "130" " 90" "154"
## [304,] "Ho-oh" " 680" "106" "130" " 90" "110" "154"
## [305,] "Celebi" " 600" "100" "100" "100" "100" "100"
## [306,] "Treecko" " 310" " 40" " 45" " 35" " 65" " 55"
## [307,] "Grovyle" " 405" " 50" " 65" " 45" " 85" " 65"
## [308,] "Sceptile" " 530" " 70" " 85" " 65" "105" " 85"
## [309,] "Mega Sceptile" " 630" " 70" "110" " 75" "145" " 85"
## [310,] "Torchic" " 310" " 45" " 60" " 40" " 70" " 50"
## [311,] "Combusken" " 405" " 60" " 85" " 60" " 85" " 60"
## [312,] "Blaziken" " 530" " 80" "120" " 70" "110" " 70"
## [313,] "Mega Blaziken" " 630" " 80" "160" " 80" "130" " 80"
## [314,] "Mudkip" " 310" " 50" " 70" " 50" " 50" " 50"
## [315,] "Marshtomp" " 405" " 70" " 85" " 70" " 60" " 70"
## [316,] "Swampert" " 535" "100" "110" " 90" " 85" " 90"
## [317,] "Mega Swampert" " 635" "100" "150" "110" " 95" "110"
## [318,] "Poochyena" " 220" " 35" " 55" " 35" " 30" " 30"
## [319,] "Mightyena" " 420" " 70" " 90" " 70" " 60" " 60"
## [320,] "Zigzagoon" " 240" " 38" " 30" " 41" " 30" " 41"
## [321,] "Mega Zigzagoon" " 240" " 38" " 30" " 41" " 30" " 41"
## [322,] "Linoone" " 420" " 78" " 70" " 61" " 50" " 61"
## [323,] "Mega Linoone" " 420" " 78" " 70" " 61" " 50" " 61"
## [324,] "Wurmple" " 195" " 45" " 45" " 35" " 20" " 30"
## [325,] "Silcoon" " 205" " 50" " 35" " 55" " 25" " 25"
## [326,] "Beautifly" " 395" " 60" " 70" " 50" "100" " 50"
## [327,] "Cascoon" " 205" " 50" " 35" " 55" " 25" " 25"
## [328,] "Dustox" " 385" " 60" " 50" " 70" " 50" " 90"
## [329,] "Lotad" " 220" " 40" " 30" " 30" " 40" " 50"
## [330,] "Lombre" " 340" " 60" " 50" " 50" " 60" " 70"
## [331,] "Ludicolo" " 480" " 80" " 70" " 70" " 90" "100"
## [332,] "Seedot" " 220" " 40" " 40" " 50" " 30" " 30"
## [333,] "Nuzleaf" " 340" " 70" " 70" " 40" " 60" " 40"
## [334,] "Shiftry" " 480" " 90" "100" " 60" " 90" " 60"
## [335,] "Taillow" " 270" " 40" " 55" " 30" " 30" " 30"
## [336,] "Swellow" " 455" " 60" " 85" " 60" " 75" " 50"
## [337,] "Wingull" " 270" " 40" " 30" " 30" " 55" " 30"
## [338,] "Pelipper" " 440" " 60" " 50" "100" " 95" " 70"
## [339,] "Ralts" " 198" " 28" " 25" " 25" " 45" " 35"
## [340,] "Kirlia" " 278" " 38" " 35" " 35" " 65" " 55"
## [341,] "Gardevoir" " 518" " 68" " 65" " 65" "125" "115"
## [342,] "Mega Gardevoir" " 618" " 68" " 85" " 65" "165" "135"
## [343,] "Surskit" " 269" " 40" " 30" " 32" " 50" " 52"
## [344,] "Masquerain" " 454" " 70" " 60" " 62" "100" " 82"
## [345,] "Shroomish" " 295" " 60" " 40" " 60" " 40" " 60"
## [346,] "Breloom" " 460" " 60" "130" " 80" " 60" " 60"
## [347,] "Slakoth" " 280" " 60" " 60" " 60" " 35" " 35"
## [348,] "Vigoroth" " 440" " 80" " 80" " 80" " 55" " 55"
## [349,] "Slaking" " 670" "150" "160" "100" " 95" " 65"
## [350,] "Nincada" " 266" " 31" " 45" " 90" " 30" " 30"
## [351,] "Ninjask" " 456" " 61" " 90" " 45" " 50" " 50"
## [352,] "Shedinja" " 236" " 1" " 90" " 45" " 30" " 30"
## [353,] "Whismur" " 240" " 64" " 51" " 23" " 51" " 23"
## [354,] "Loudred" " 360" " 84" " 71" " 43" " 71" " 43"
## [355,] "Exploud" " 490" "104" " 91" " 63" " 91" " 73"
## [356,] "Makuhita" " 237" " 72" " 60" " 30" " 20" " 30"
## [357,] "Hariyama" " 474" "144" "120" " 60" " 40" " 60"
## [358,] "Azurill" " 190" " 50" " 20" " 40" " 20" " 40"
## [359,] "Nosepass" " 375" " 30" " 45" "135" " 45" " 90"
## [360,] "Skitty" " 260" " 50" " 45" " 45" " 35" " 35"
## [361,] "Delcatty" " 400" " 70" " 65" " 65" " 55" " 55"
## [362,] "Sableye" " 380" " 50" " 75" " 75" " 65" " 65"
## [363,] "Mega Sableye" " 480" " 50" " 85" "125" " 85" "115"
## [364,] "Mawile" " 380" " 50" " 85" " 85" " 55" " 55"
## [365,] "Mega Mawile" " 480" " 50" "105" "125" " 55" " 95"
## [366,] "Aron" " 330" " 50" " 70" "100" " 40" " 40"
## [367,] "Lairon" " 430" " 60" " 90" "140" " 50" " 50"
## [368,] "Aggron" " 530" " 70" "110" "180" " 60" " 60"
## [369,] "Mega Aggron" " 630" " 70" "140" "230" " 60" " 80"
## [370,] "Meditite" " 280" " 30" " 40" " 55" " 40" " 55"
## [371,] "Medicham" " 410" " 60" " 60" " 75" " 60" " 75"
## [372,] "Mega Medicham" " 510" " 60" "100" " 85" " 80" " 85"
## [373,] "Electrike" " 295" " 40" " 45" " 40" " 65" " 40"
## [374,] "Manectric" " 475" " 70" " 75" " 60" "105" " 60"
## [375,] "Mega Manectric" " 575" " 70" " 75" " 80" "135" " 80"
## [376,] "Plusle" " 405" " 60" " 50" " 40" " 85" " 75"
## [377,] "Minun" " 405" " 60" " 40" " 50" " 75" " 85"
## [378,] "Volbeat" " 430" " 65" " 73" " 75" " 47" " 85"
## [379,] "Illumise" " 430" " 65" " 47" " 75" " 73" " 85"
## [380,] "Roselia" " 400" " 50" " 60" " 45" "100" " 80"
## [381,] "Gulpin" " 302" " 70" " 43" " 53" " 43" " 53"
## [382,] "Swalot" " 467" "100" " 73" " 83" " 73" " 83"
## [383,] "Carvanha" " 305" " 45" " 90" " 20" " 65" " 20"
## [384,] "Sharpedo" " 460" " 70" "120" " 40" " 95" " 40"
## [385,] "Mega Sharpedo" " 560" " 70" "140" " 70" "110" " 65"
## [386,] "Wailmer" " 400" "130" " 70" " 35" " 70" " 35"
## [387,] "Wailord" " 500" "170" " 90" " 45" " 90" " 45"
## [388,] "Numel" " 305" " 60" " 60" " 40" " 65" " 45"
## [389,] "Camerupt" " 460" " 70" "100" " 70" "105" " 75"
## [390,] "Mega Camerupt" " 560" " 70" "120" "100" "145" "105"
## [391,] "Torkoal" " 470" " 70" " 85" "140" " 85" " 70"
## [392,] "Spoink" " 330" " 60" " 25" " 35" " 70" " 80"
## [393,] "Grumpig" " 470" " 80" " 45" " 65" " 90" "110"
## [394,] "Spinda" " 360" " 60" " 60" " 60" " 60" " 60"
## [395,] "Trapinch" " 290" " 45" "100" " 45" " 45" " 45"
## [396,] "Vibrava" " 340" " 50" " 70" " 50" " 50" " 50"
## [397,] "Flygon" " 520" " 80" "100" " 80" " 80" " 80"
## [398,] "Cacnea" " 335" " 50" " 85" " 40" " 85" " 40"
## [399,] "Cacturne" " 475" " 70" "115" " 60" "115" " 60"
## [400,] "Swablu" " 310" " 45" " 40" " 60" " 40" " 75"
## [401,] "Altaria" " 490" " 75" " 70" " 90" " 70" "105"
## [402,] "Mega Altaria" " 590" " 75" "110" "110" "110" "105"
## [403,] "Zangoose" " 458" " 73" "115" " 60" " 60" " 60"
## [404,] "Seviper" " 458" " 73" "100" " 60" "100" " 60"
## [405,] "Lunatone" " 460" " 90" " 55" " 65" " 95" " 85"
## [406,] "Solrock" " 460" " 90" " 95" " 85" " 55" " 65"
## [407,] "Barboach" " 288" " 50" " 48" " 43" " 46" " 41"
## [408,] "Whiscash" " 468" "110" " 78" " 73" " 76" " 71"
## [409,] "Corphish" " 308" " 43" " 80" " 65" " 50" " 35"
## [410,] "Crawdaunt" " 468" " 63" "120" " 85" " 90" " 55"
## [411,] "Baltoy" " 300" " 40" " 40" " 55" " 40" " 70"
## [412,] "Claydol" " 500" " 60" " 70" "105" " 70" "120"
## [413,] "Lileep" " 355" " 66" " 41" " 77" " 61" " 87"
## [414,] "Cradily" " 495" " 86" " 81" " 97" " 81" "107"
## [415,] "Anorith" " 355" " 45" " 95" " 50" " 40" " 50"
## [416,] "Armaldo" " 495" " 75" "125" "100" " 70" " 80"
## [417,] "Feebas" " 200" " 20" " 15" " 20" " 10" " 55"
## [418,] "Milotic" " 540" " 95" " 60" " 79" "100" "125"
## [419,] "Castform" " 420" " 70" " 70" " 70" " 70" " 70"
## [420,] "Mega Castform" " 420" " 70" " 70" " 70" " 70" " 70"
## [421,] "Mega Castform X" " 420" " 70" " 70" " 70" " 70" " 70"
## [422,] "Mega Castform X" " 420" " 70" " 70" " 70" " 70" " 70"
## [423,] "Kecleon" " 440" " 60" " 90" " 70" " 60" "120"
## [424,] "Shuppet" " 295" " 44" " 75" " 35" " 63" " 33"
## [425,] "Banette" " 455" " 64" "115" " 65" " 83" " 63"
## [426,] "Mega Banette" " 555" " 64" "165" " 75" " 93" " 83"
## [427,] "Duskull" " 295" " 20" " 40" " 90" " 30" " 90"
## [428,] "Dusclops" " 455" " 40" " 70" "130" " 60" "130"
## [429,] "Tropius" " 460" " 99" " 68" " 83" " 72" " 87"
## [430,] "Chimecho" " 455" " 75" " 50" " 80" " 95" " 90"
## [431,] "Absol" " 465" " 65" "130" " 60" " 75" " 60"
## [432,] "Mega Absol" " 565" " 65" "150" " 60" "115" " 60"
## [433,] "Wynaut" " 260" " 95" " 23" " 48" " 23" " 48"
## [434,] "Snorunt" " 300" " 50" " 50" " 50" " 50" " 50"
## [435,] "Glalie" " 480" " 80" " 80" " 80" " 80" " 80"
## [436,] "Mega Glalie" " 580" " 80" "120" " 80" "120" " 80"
## [437,] "Spheal" " 290" " 70" " 40" " 50" " 55" " 50"
## [438,] "Sealeo" " 410" " 90" " 60" " 70" " 75" " 70"
## [439,] "Walrein" " 530" "110" " 80" " 90" " 95" " 90"
## [440,] "Clamperl" " 345" " 35" " 64" " 85" " 74" " 55"
## [441,] "Huntail" " 485" " 55" "104" "105" " 94" " 75"
## [442,] "Gorebyss" " 485" " 55" " 84" "105" "114" " 75"
## [443,] "Relicanth" " 485" "100" " 90" "130" " 45" " 65"
## [444,] "Luvdisc" " 330" " 43" " 30" " 55" " 40" " 65"
## [445,] "Bagon" " 300" " 45" " 75" " 60" " 40" " 30"
## [446,] "Shelgon" " 420" " 65" " 95" "100" " 60" " 50"
## [447,] "Salamence" " 600" " 95" "135" " 80" "110" " 80"
## [448,] "Mega Salamence" " 700" " 95" "145" "130" "120" " 90"
## [449,] "Beldum" " 300" " 40" " 55" " 80" " 35" " 60"
## [450,] "Metang" " 420" " 60" " 75" "100" " 55" " 80"
## [451,] "Metagross" " 600" " 80" "135" "130" " 95" " 90"
## [452,] "Mega Metagross" " 700" " 80" "145" "150" "105" "110"
## [453,] "Regirock" " 580" " 80" "100" "200" " 50" "100"
## [454,] "Regice" " 580" " 80" " 50" "100" "100" "200"
## [455,] "Registeel" " 580" " 80" " 75" "150" " 75" "150"
## [456,] "Latias" " 600" " 80" " 80" " 90" "110" "130"
## [457,] "Mega Latias" " 700" " 80" "100" "120" "140" "150"
## [458,] "Latios" " 600" " 80" " 90" " 80" "130" "110"
## [459,] "Mega Latios" " 700" " 80" "130" "100" "160" "120"
## [460,] "Kyogre" " 670" "100" "100" " 90" "150" "140"
## [461,] "Mega Kyogre" " 770" "100" "150" " 90" "180" "160"
## [462,] "Groudon" " 670" "100" "150" "140" "100" " 90"
## [463,] "Mega Groudon" " 770" "100" "180" "160" "150" " 90"
## [464,] "Rayquaza" " 680" "105" "150" " 90" "150" " 90"
## [465,] "Mega Rayquaza" " 780" "105" "180" "100" "180" "100"
## [466,] "Jirachi" " 600" "100" "100" "100" "100" "100"
## [467,] "Deoxys" " 600" " 50" "150" " 50" "150" " 50"
## [468,] "Mega Deoxys" " 600" " 50" "180" " 20" "180" " 20"
## [469,] "Mega Deoxys X" " 600" " 50" " 70" "160" " 70" "160"
## [470,] "Mega Deoxys X" " 600" " 50" " 95" " 90" " 95" " 90"
## [471,] "Turtwig" " 318" " 55" " 68" " 64" " 45" " 55"
## [472,] "Grotle" " 405" " 75" " 89" " 85" " 55" " 65"
## [473,] "Torterra" " 525" " 95" "109" "105" " 75" " 85"
## [474,] "Chimchar" " 309" " 44" " 58" " 44" " 58" " 44"
## [475,] "Monferno" " 405" " 64" " 78" " 52" " 78" " 52"
## [476,] "Infernape" " 534" " 76" "104" " 71" "104" " 71"
## [477,] "Piplup" " 314" " 53" " 51" " 53" " 61" " 56"
## [478,] "Prinplup" " 405" " 64" " 66" " 68" " 81" " 76"
## [479,] "Empoleon" " 530" " 84" " 86" " 88" "111" "101"
## [480,] "Starly" " 245" " 40" " 55" " 30" " 30" " 30"
## [481,] "Staravia" " 340" " 55" " 75" " 50" " 40" " 40"
## [482,] "Staraptor" " 485" " 85" "120" " 70" " 50" " 60"
## [483,] "Bidoof" " 250" " 59" " 45" " 40" " 35" " 40"
## [484,] "Bibarel" " 410" " 79" " 85" " 60" " 55" " 60"
## [485,] "Kricketot" " 194" " 37" " 25" " 41" " 25" " 41"
## [486,] "Kricketune" " 384" " 77" " 85" " 51" " 55" " 51"
## [487,] "Shinx" " 263" " 45" " 65" " 34" " 40" " 34"
## [488,] "Luxio" " 363" " 60" " 85" " 49" " 60" " 49"
## [489,] "Luxray" " 523" " 80" "120" " 79" " 95" " 79"
## [490,] "Budew" " 280" " 40" " 30" " 35" " 50" " 70"
## [491,] "Roserade" " 515" " 60" " 70" " 65" "125" "105"
## [492,] "Cranidos" " 350" " 67" "125" " 40" " 30" " 30"
## [493,] "Rampardos" " 495" " 97" "165" " 60" " 65" " 50"
## [494,] "Shieldon" " 350" " 30" " 42" "118" " 42" " 88"
## [495,] "Bastiodon" " 495" " 60" " 52" "168" " 47" "138"
## [496,] "Burmy" " 224" " 40" " 29" " 45" " 29" " 45"
## [497,] "Wormadam" " 424" " 60" " 59" " 85" " 79" "105"
## [498,] "Mega Wormadam" " 424" " 60" " 79" "105" " 59" " 85"
## [499,] "Mega Wormadam X" " 424" " 60" " 69" " 95" " 69" " 95"
## [500,] "Mothim" " 424" " 70" " 94" " 50" " 94" " 50"
## [501,] "Combee" " 244" " 30" " 30" " 42" " 30" " 42"
## [502,] "Vespiquen" " 474" " 70" " 80" "102" " 80" "102"
## [503,] "Pachirisu" " 405" " 60" " 45" " 70" " 45" " 90"
## [504,] "Buizel" " 330" " 55" " 65" " 35" " 60" " 30"
## [505,] "Floatzel" " 495" " 85" "105" " 55" " 85" " 50"
## [506,] "Cherubi" " 275" " 45" " 35" " 45" " 62" " 53"
## [507,] "Cherrim" " 450" " 70" " 60" " 70" " 87" " 78"
## [508,] "Shellos" " 325" " 76" " 48" " 48" " 57" " 62"
## [509,] "Gastrodon" " 475" "111" " 83" " 68" " 92" " 82"
## [510,] "Ambipom" " 482" " 75" "100" " 66" " 60" " 66"
## [511,] "Drifloon" " 348" " 90" " 50" " 34" " 60" " 44"
## [512,] "Drifblim" " 498" "150" " 80" " 44" " 90" " 54"
## [513,] "Buneary" " 350" " 55" " 66" " 44" " 44" " 56"
## [514,] "Lopunny" " 480" " 65" " 76" " 84" " 54" " 96"
## [515,] "Mega Lopunny" " 580" " 65" "136" " 94" " 54" " 96"
## [516,] "Mismagius" " 495" " 60" " 60" " 60" "105" "105"
## [517,] "Honchkrow" " 505" "100" "125" " 52" "105" " 52"
## [518,] "Glameow" " 310" " 49" " 55" " 42" " 42" " 37"
## [519,] "Purugly" " 452" " 71" " 82" " 64" " 64" " 59"
## [520,] "Chingling" " 285" " 45" " 30" " 50" " 65" " 50"
## [521,] "Stunky" " 329" " 63" " 63" " 47" " 41" " 41"
## [522,] "Skuntank" " 479" "103" " 93" " 67" " 71" " 61"
## [523,] "Bronzor" " 300" " 57" " 24" " 86" " 24" " 86"
## [524,] "Bronzong" " 500" " 67" " 89" "116" " 79" "116"
## [525,] "Bonsly" " 290" " 50" " 80" " 95" " 10" " 45"
## [526,] "Mime Jr." " 310" " 20" " 25" " 45" " 70" " 90"
## [527,] "Happiny" " 220" "100" " 5" " 5" " 15" " 65"
## [528,] "Chatot" " 411" " 76" " 65" " 45" " 92" " 42"
## [529,] "Spiritomb" " 485" " 50" " 92" "108" " 92" "108"
## [530,] "Gible" " 300" " 58" " 70" " 45" " 40" " 45"
## [531,] "Gabite" " 410" " 68" " 90" " 65" " 50" " 55"
## [532,] "Garchomp" " 600" "108" "130" " 95" " 80" " 85"
## [533,] "Mega Garchomp" " 700" "108" "170" "115" "120" " 95"
## [534,] "Munchlax" " 390" "135" " 85" " 40" " 40" " 85"
## [535,] "Riolu" " 285" " 40" " 70" " 40" " 35" " 40"
## [536,] "Lucario" " 525" " 70" "110" " 70" "115" " 70"
## [537,] "Mega Lucario" " 625" " 70" "145" " 88" "140" " 70"
## [538,] "Hippopotas" " 330" " 68" " 72" " 78" " 38" " 42"
## [539,] "Hippowdon" " 525" "108" "112" "118" " 68" " 72"
## [540,] "Skorupi" " 330" " 40" " 50" " 90" " 30" " 55"
## [541,] "Drapion" " 500" " 70" " 90" "110" " 60" " 75"
## [542,] "Croagunk" " 300" " 48" " 61" " 40" " 61" " 40"
## [543,] "Toxicroak" " 490" " 83" "106" " 65" " 86" " 65"
## [544,] "Carnivine" " 454" " 74" "100" " 72" " 90" " 72"
## [545,] "Finneon" " 330" " 49" " 49" " 56" " 49" " 61"
## [546,] "Lumineon" " 460" " 69" " 69" " 76" " 69" " 86"
## [547,] "Mantyke" " 345" " 45" " 20" " 50" " 60" "120"
## [548,] "Snover" " 334" " 60" " 62" " 50" " 62" " 60"
## [549,] "Abomasnow" " 494" " 90" " 92" " 75" " 92" " 85"
## [550,] "Mega Abomasnow" " 594" " 90" "132" "105" "132" "105"
## [551,] "Weavile" " 510" " 70" "120" " 65" " 45" " 85"
## [552,] "Magnezone" " 535" " 70" " 70" "115" "130" " 90"
## [553,] "Lickilicky" " 515" "110" " 85" " 95" " 80" " 95"
## [554,] "Rhyperior" " 535" "115" "140" "130" " 55" " 55"
## [555,] "Tangrowth" " 535" "100" "100" "125" "110" " 50"
## [556,] "Electivire" " 540" " 75" "123" " 67" " 95" " 85"
## [557,] "Magmortar" " 540" " 75" " 95" " 67" "125" " 95"
## [558,] "Togekiss" " 545" " 85" " 50" " 95" "120" "115"
## [559,] "Yanmega" " 515" " 86" " 76" " 86" "116" " 56"
## [560,] "Leafeon" " 525" " 65" "110" "130" " 60" " 65"
## [561,] "Glaceon" " 525" " 65" " 60" "110" "130" " 95"
## [562,] "Gliscor" " 510" " 75" " 95" "125" " 45" " 75"
## [563,] "Mamoswine" " 530" "110" "130" " 80" " 70" " 60"
## [564,] "Porygon-Z" " 535" " 85" " 80" " 70" "135" " 75"
## [565,] "Gallade" " 518" " 68" "125" " 65" " 65" "115"
## [566,] "Mega Gallade" " 618" " 68" "165" " 95" " 65" "115"
## [567,] "Probopass" " 525" " 60" " 55" "145" " 75" "150"
## [568,] "Dusknoir" " 525" " 45" "100" "135" " 65" "135"
## [569,] "Froslass" " 480" " 70" " 80" " 70" " 80" " 70"
## [570,] "Rotom" " 440" " 50" " 50" " 77" " 95" " 77"
## [571,] "Mega Rotom" " 520" " 50" " 65" "107" "105" "107"
## [572,] "Mega Rotom X" " 520" " 50" " 65" "107" "105" "107"
## [573,] "Mega Rotom X" " 520" " 50" " 65" "107" "105" "107"
## [574,] "Mega Rotom X" " 520" " 50" " 65" "107" "105" "107"
## [575,] "Mega Rotom X" " 520" " 50" " 65" "107" "105" "107"
## [576,] "Uxie" " 580" " 75" " 75" "130" " 75" "130"
## [577,] "Mesprit" " 580" " 80" "105" "105" "105" "105"
## [578,] "Azelf" " 580" " 75" "125" " 70" "125" " 70"
## [579,] "Dialga" " 680" "100" "120" "120" "150" "100"
## [580,] "Palkia" " 680" " 90" "120" "100" "150" "120"
## [581,] "Heatran" " 600" " 91" " 90" "106" "130" "106"
## [582,] "Regigigas" " 670" "110" "160" "110" " 80" "110"
## [583,] "Giratina" " 680" "150" "100" "120" "100" "120"
## [584,] "Mega Giratina" " 680" "150" "120" "100" "120" "100"
## [585,] "Cresselia" " 600" "120" " 70" "120" " 75" "130"
## [586,] "Phione" " 480" " 80" " 80" " 80" " 80" " 80"
## [587,] "Manaphy" " 600" "100" "100" "100" "100" "100"
## [588,] "Darkrai" " 600" " 70" " 90" " 90" "135" " 90"
## [589,] "Shaymin" " 600" "100" "100" "100" "100" "100"
## [590,] "Mega Shaymin" " 600" "100" "103" " 75" "120" " 75"
## [591,] "Arceus" " 720" "120" "120" "120" "120" "120"
## [592,] "Victini" " 600" "100" "100" "100" "100" "100"
## [593,] "Snivy" " 308" " 45" " 45" " 55" " 45" " 55"
## [594,] "Servine" " 413" " 60" " 60" " 75" " 60" " 75"
## [595,] "Serperior" " 528" " 75" " 75" " 95" " 75" " 95"
## [596,] "Tepig" " 308" " 65" " 63" " 45" " 45" " 45"
## [597,] "Pignite" " 418" " 90" " 93" " 55" " 70" " 55"
## [598,] "Emboar" " 528" "110" "123" " 65" "100" " 65"
## [599,] "Oshawott" " 308" " 55" " 55" " 45" " 63" " 45"
## [600,] "Dewott" " 413" " 75" " 75" " 60" " 83" " 60"
## [601,] "Samurott" " 528" " 95" "100" " 85" "108" " 70"
## [602,] "Patrat" " 255" " 45" " 55" " 39" " 35" " 39"
## [603,] "Watchog" " 420" " 60" " 85" " 69" " 60" " 69"
## [604,] "Lillipup" " 275" " 45" " 60" " 45" " 25" " 45"
## [605,] "Herdier" " 370" " 65" " 80" " 65" " 35" " 65"
## [606,] "Stoutland" " 500" " 85" "110" " 90" " 45" " 90"
## [607,] "Purrloin" " 281" " 41" " 50" " 37" " 50" " 37"
## [608,] "Liepard" " 446" " 64" " 88" " 50" " 88" " 50"
## [609,] "Pansage" " 316" " 50" " 53" " 48" " 53" " 48"
## [610,] "Simisage" " 498" " 75" " 98" " 63" " 98" " 63"
## [611,] "Pansear" " 316" " 50" " 53" " 48" " 53" " 48"
## [612,] "Simisear" " 498" " 75" " 98" " 63" " 98" " 63"
## [613,] "Panpour" " 316" " 50" " 53" " 48" " 53" " 48"
## [614,] "Simipour" " 498" " 75" " 98" " 63" " 98" " 63"
## [615,] "Munna" " 292" " 76" " 25" " 45" " 67" " 55"
## [616,] "Musharna" " 487" "116" " 55" " 85" "107" " 95"
## [617,] "Pidove" " 264" " 50" " 55" " 50" " 36" " 30"
## [618,] "Tranquill" " 358" " 62" " 77" " 62" " 50" " 42"
## [619,] "Unfezant" " 488" " 80" "115" " 80" " 65" " 55"
## [620,] "Blitzle" " 295" " 45" " 60" " 32" " 50" " 32"
## [621,] "Zebstrika" " 497" " 75" "100" " 63" " 80" " 63"
## [622,] "Roggenrola" " 280" " 55" " 75" " 85" " 25" " 25"
## [623,] "Boldore" " 390" " 70" "105" "105" " 50" " 40"
## [624,] "Gigalith" " 515" " 85" "135" "130" " 60" " 80"
## [625,] "Woobat" " 323" " 65" " 45" " 43" " 55" " 43"
## [626,] "Swoobat" " 425" " 67" " 57" " 55" " 77" " 55"
## [627,] "Drilbur" " 328" " 60" " 85" " 40" " 30" " 45"
## [628,] "Excadrill" " 508" "110" "135" " 60" " 50" " 65"
## [629,] "Audino" " 445" "103" " 60" " 86" " 60" " 86"
## [630,] "Mega Audino" " 545" "103" " 60" "126" " 80" "126"
## [631,] "Timburr" " 305" " 75" " 80" " 55" " 25" " 35"
## [632,] "Gurdurr" " 405" " 85" "105" " 85" " 40" " 50"
## [633,] "Conkeldurr" " 505" "105" "140" " 95" " 55" " 65"
## [634,] "Tympole" " 294" " 50" " 50" " 40" " 50" " 40"
## [635,] "Palpitoad" " 384" " 75" " 65" " 55" " 65" " 55"
## [636,] "Seismitoad" " 509" "105" " 95" " 75" " 85" " 75"
## [637,] "Throh" " 465" "120" "100" " 85" " 30" " 85"
## [638,] "Sawk" " 465" " 75" "125" " 75" " 30" " 75"
## [639,] "Sewaddle" " 310" " 45" " 53" " 70" " 40" " 60"
## [640,] "Swadloon" " 380" " 55" " 63" " 90" " 50" " 80"
## [641,] "Leavanny" " 500" " 75" "103" " 80" " 70" " 80"
## [642,] "Venipede" " 260" " 30" " 45" " 59" " 30" " 39"
## [643,] "Whirlipede" " 360" " 40" " 55" " 99" " 40" " 79"
## [644,] "Scolipede" " 485" " 60" "100" " 89" " 55" " 69"
## [645,] "Cottonee" " 280" " 40" " 27" " 60" " 37" " 50"
## [646,] "Whimsicott" " 480" " 60" " 67" " 85" " 77" " 75"
## [647,] "Petilil" " 280" " 45" " 35" " 50" " 70" " 50"
## [648,] "Lilligant" " 480" " 70" " 60" " 75" "110" " 75"
## [649,] "Basculin" " 460" " 70" " 92" " 65" " 80" " 55"
## [650,] "Mega Basculin" " 460" " 70" " 92" " 65" " 80" " 55"
## [651,] "Sandile" " 292" " 50" " 72" " 35" " 35" " 35"
## [652,] "Krokorok" " 351" " 60" " 82" " 45" " 45" " 45"
## [653,] "Krookodile" " 519" " 95" "117" " 80" " 65" " 70"
## [654,] "Darumaka" " 315" " 70" " 90" " 45" " 15" " 45"
## [655,] "Mega Darumaka" " 315" " 70" " 90" " 45" " 15" " 45"
## [656,] "Darmanitan" " 480" "105" "140" " 55" " 30" " 55"
## [657,] "Mega Darmanitan" " 540" "105" " 30" "105" "140" "105"
## [658,] "Mega Darmanitan X" " 480" "105" "140" " 55" " 30" " 55"
## [659,] "Mega Darmanitan X" " 540" "105" "160" " 55" " 30" " 55"
## [660,] "Maractus" " 461" " 75" " 86" " 67" "106" " 67"
## [661,] "Dwebble" " 325" " 50" " 65" " 85" " 35" " 35"
## [662,] "Crustle" " 485" " 70" "105" "125" " 65" " 75"
## [663,] "Scraggy" " 348" " 50" " 75" " 70" " 35" " 70"
## [664,] "Scrafty" " 488" " 65" " 90" "115" " 45" "115"
## [665,] "Sigilyph" " 490" " 72" " 58" " 80" "103" " 80"
## [666,] "Yamask" " 303" " 38" " 30" " 85" " 55" " 65"
## [667,] "Mega Yamask" " 303" " 38" " 55" " 85" " 30" " 65"
## [668,] "Cofagrigus" " 483" " 58" " 50" "145" " 95" "105"
## [669,] "Tirtouga" " 355" " 54" " 78" "103" " 53" " 45"
## [670,] "Carracosta" " 495" " 74" "108" "133" " 83" " 65"
## [671,] "Archen" " 401" " 55" "112" " 45" " 74" " 45"
## [672,] "Archeops" " 567" " 75" "140" " 65" "112" " 65"
## [673,] "Trubbish" " 329" " 50" " 50" " 62" " 40" " 62"
## [674,] "Garbodor" " 474" " 80" " 95" " 82" " 60" " 82"
## [675,] "Zorua" " 330" " 40" " 65" " 40" " 80" " 40"
## [676,] "Zoroark" " 510" " 60" "105" " 60" "120" " 60"
## [677,] "Minccino" " 300" " 55" " 50" " 40" " 40" " 40"
## [678,] "Cinccino" " 470" " 75" " 95" " 60" " 65" " 60"
## [679,] "Gothita" " 290" " 45" " 30" " 50" " 55" " 65"
## [680,] "Gothorita" " 390" " 60" " 45" " 70" " 75" " 85"
## [681,] "Gothitelle" " 490" " 70" " 55" " 95" " 95" "110"
## [682,] "Solosis" " 290" " 45" " 30" " 40" "105" " 50"
## [683,] "Duosion" " 370" " 65" " 40" " 50" "125" " 60"
## [684,] "Reuniclus" " 490" "110" " 65" " 75" "125" " 85"
## [685,] "Ducklett" " 305" " 62" " 44" " 50" " 44" " 50"
## [686,] "Swanna" " 473" " 75" " 87" " 63" " 87" " 63"
## [687,] "Vanillite" " 305" " 36" " 50" " 50" " 65" " 60"
## [688,] "Vanillish" " 395" " 51" " 65" " 65" " 80" " 75"
## [689,] "Vanilluxe" " 535" " 71" " 95" " 85" "110" " 95"
## [690,] "Deerling" " 335" " 60" " 60" " 50" " 40" " 50"
## [691,] "Sawsbuck" " 475" " 80" "100" " 70" " 60" " 70"
## [692,] "Emolga" " 428" " 55" " 75" " 60" " 75" " 60"
## [693,] "Karrablast" " 315" " 50" " 75" " 45" " 40" " 45"
## [694,] "Escavalier" " 495" " 70" "135" "105" " 60" "105"
## [695,] "Foongus" " 294" " 69" " 55" " 45" " 55" " 55"
## [696,] "Amoonguss" " 464" "114" " 85" " 70" " 85" " 80"
## [697,] "Frillish" " 335" " 55" " 40" " 50" " 65" " 85"
## [698,] "Jellicent" " 480" "100" " 60" " 70" " 85" "105"
## [699,] "Alomomola" " 470" "165" " 75" " 80" " 40" " 45"
## [700,] "Joltik" " 319" " 50" " 47" " 50" " 57" " 50"
## [701,] "Galvantula" " 472" " 70" " 77" " 60" " 97" " 60"
## [702,] "Ferroseed" " 305" " 44" " 50" " 91" " 24" " 86"
## [703,] "Ferrothorn" " 489" " 74" " 94" "131" " 54" "116"
## [704,] "Klink" " 300" " 40" " 55" " 70" " 45" " 60"
## [705,] "Klang" " 440" " 60" " 80" " 95" " 70" " 85"
## [706,] "Klinklang" " 520" " 60" "100" "115" " 70" " 85"
## [707,] "Tynamo" " 275" " 35" " 55" " 40" " 45" " 40"
## [708,] "Eelektrik" " 405" " 65" " 85" " 70" " 75" " 70"
## [709,] "Eelektross" " 515" " 85" "115" " 80" "105" " 80"
## [710,] "Elgyem" " 335" " 55" " 55" " 55" " 85" " 55"
## [711,] "Beheeyem" " 485" " 75" " 75" " 75" "125" " 95"
## [712,] "Litwick" " 275" " 50" " 30" " 55" " 65" " 55"
## [713,] "Lampent" " 370" " 60" " 40" " 60" " 95" " 60"
## [714,] "Chandelure" " 520" " 60" " 55" " 90" "145" " 90"
## [715,] "Axew" " 320" " 46" " 87" " 60" " 30" " 40"
## [716,] "Fraxure" " 410" " 66" "117" " 70" " 40" " 50"
## [717,] "Haxorus" " 540" " 76" "147" " 90" " 60" " 70"
## [718,] "Cubchoo" " 305" " 55" " 70" " 40" " 60" " 40"
## [719,] "Beartic" " 505" " 95" "130" " 80" " 70" " 80"
## [720,] "Cryogonal" " 515" " 80" " 50" " 50" " 95" "135"
## [721,] "Shelmet" " 305" " 50" " 40" " 85" " 40" " 65"
## [722,] "Accelgor" " 495" " 80" " 70" " 40" "100" " 60"
## [723,] "Stunfisk" " 471" "109" " 66" " 84" " 81" " 99"
## [724,] "Mega Stunfisk" " 471" "109" " 81" " 99" " 66" " 84"
## [725,] "Mienfoo" " 350" " 45" " 85" " 50" " 55" " 50"
## [726,] "Mienshao" " 510" " 65" "125" " 60" " 95" " 60"
## [727,] "Druddigon" " 485" " 77" "120" " 90" " 60" " 90"
## [728,] "Golett" " 303" " 59" " 74" " 50" " 35" " 50"
## [729,] "Golurk" " 483" " 89" "124" " 80" " 55" " 80"
## [730,] "Pawniard" " 340" " 45" " 85" " 70" " 40" " 40"
## [731,] "Bisharp" " 490" " 65" "125" "100" " 60" " 70"
## [732,] "Bouffalant" " 490" " 95" "110" " 95" " 40" " 95"
## [733,] "Rufflet" " 350" " 70" " 83" " 50" " 37" " 50"
## [734,] "Braviary" " 510" "100" "123" " 75" " 57" " 75"
## [735,] "Vullaby" " 370" " 70" " 55" " 75" " 45" " 65"
## [736,] "Mandibuzz" " 510" "110" " 65" "105" " 55" " 95"
## [737,] "Heatmor" " 484" " 85" " 97" " 66" "105" " 66"
## [738,] "Durant" " 484" " 58" "109" "112" " 48" " 48"
## [739,] "Deino" " 300" " 52" " 65" " 50" " 45" " 50"
## [740,] "Zweilous" " 420" " 72" " 85" " 70" " 65" " 70"
## [741,] "Hydreigon" " 600" " 92" "105" " 90" "125" " 90"
## [742,] "Larvesta" " 360" " 55" " 85" " 55" " 50" " 55"
## [743,] "Volcarona" " 550" " 85" " 60" " 65" "135" "105"
## [744,] "Cobalion" " 580" " 91" " 90" "129" " 90" " 72"
## [745,] "Terrakion" " 580" " 91" "129" " 90" " 72" " 90"
## [746,] "Virizion" " 580" " 91" " 90" " 72" " 90" "129"
## [747,] "Tornadus" " 580" " 79" "115" " 70" "125" " 80"
## [748,] "Mega Tornadus" " 580" " 79" "100" " 80" "110" " 90"
## [749,] "Thundurus" " 580" " 79" "115" " 70" "125" " 80"
## [750,] "Mega Thundurus" " 580" " 79" "105" " 70" "145" " 80"
## [751,] "Reshiram" " 680" "100" "120" "100" "150" "120"
## [752,] "Zekrom" " 680" "100" "150" "120" "120" "100"
## [753,] "Landorus" " 600" " 89" "125" " 90" "115" " 80"
## [754,] "Mega Landorus" " 600" " 89" "145" " 90" "105" " 80"
## [755,] "Kyurem" " 660" "125" "130" " 90" "130" " 90"
## [756,] "Mega Kyurem" " 700" "125" "170" "100" "120" " 90"
## [757,] "Mega Kyurem X" " 700" "125" "120" " 90" "170" "100"
## [758,] "Keldeo" " 580" " 91" " 72" " 90" "129" " 90"
## [759,] "Mega Keldeo" " 580" " 91" " 72" " 90" "129" " 90"
## [760,] "Meloetta" " 600" "100" " 77" " 77" "128" "128"
## [761,] "Mega Meloetta" " 600" "100" "128" " 90" " 77" " 77"
## [762,] "Genesect" " 600" " 71" "120" " 95" "120" " 95"
## [763,] "Chespin" " 313" " 56" " 61" " 65" " 48" " 45"
## [764,] "Quilladin" " 405" " 61" " 78" " 95" " 56" " 58"
## [765,] "Chesnaught" " 530" " 88" "107" "122" " 74" " 75"
## [766,] "Fennekin" " 307" " 40" " 45" " 40" " 62" " 60"
## [767,] "Braixen" " 409" " 59" " 59" " 58" " 90" " 70"
## [768,] "Delphox" " 534" " 75" " 69" " 72" "114" "100"
## [769,] "Froakie" " 314" " 41" " 56" " 40" " 62" " 44"
## [770,] "Frogadier" " 405" " 54" " 63" " 52" " 83" " 56"
## [771,] "Greninja" " 530" " 72" " 95" " 67" "103" " 71"
## [772,] "Mega Greninja" " 640" " 72" "145" " 67" "153" " 71"
## [773,] "Bunnelby" " 237" " 38" " 36" " 38" " 32" " 36"
## [774,] "Diggersby" " 423" " 85" " 56" " 77" " 50" " 77"
## [775,] "Fletchling" " 278" " 45" " 50" " 43" " 40" " 38"
## [776,] "Fletchinder" " 382" " 62" " 73" " 55" " 56" " 52"
## [777,] "Talonflame" " 499" " 78" " 81" " 71" " 74" " 69"
## [778,] "Scatterbug" " 200" " 38" " 35" " 40" " 27" " 25"
## [779,] "Spewpa" " 213" " 45" " 22" " 60" " 27" " 30"
## [780,] "Vivillon" " 411" " 80" " 52" " 50" " 90" " 50"
## [781,] "Litleo" " 369" " 62" " 50" " 58" " 73" " 54"
## [782,] "Pyroar" " 507" " 86" " 68" " 72" "109" " 66"
## [783,] "Flabébé" " 303" " 44" " 38" " 39" " 61" " 79"
## [784,] "Floette" " 371" " 54" " 45" " 47" " 75" " 98"
## [785,] "Florges" " 552" " 78" " 65" " 68" "112" "154"
## [786,] "Skiddo" " 350" " 66" " 65" " 48" " 62" " 57"
## [787,] "Gogoat" " 531" "123" "100" " 62" " 97" " 81"
## [788,] "Pancham" " 348" " 67" " 82" " 62" " 46" " 48"
## [789,] "Pangoro" " 495" " 95" "124" " 78" " 69" " 71"
## [790,] "Furfrou" " 472" " 75" " 80" " 60" " 65" " 90"
## [791,] "Espurr" " 355" " 62" " 48" " 54" " 63" " 60"
## [792,] "Meowstic" " 466" " 74" " 48" " 76" " 83" " 81"
## [793,] "Mega Meowstic" " 466" " 74" " 48" " 76" " 83" " 81"
## [794,] "Honedge" " 325" " 45" " 80" "100" " 35" " 37"
## [795,] "Doublade" " 448" " 59" "110" "150" " 45" " 49"
## [796,] "Aegislash" " 500" " 60" " 50" "140" " 50" "140"
## [797,] "Mega Aegislash" " 500" " 60" "140" " 50" "140" " 50"
## [798,] "Spritzee" " 341" " 78" " 52" " 60" " 63" " 65"
## [799,] "Aromatisse" " 462" "101" " 72" " 72" " 99" " 89"
## [800,] "Swirlix" " 341" " 62" " 48" " 66" " 59" " 57"
## [801,] "Slurpuff" " 480" " 82" " 80" " 86" " 85" " 75"
## [802,] "Inkay" " 288" " 53" " 54" " 53" " 37" " 46"
## [803,] "Malamar" " 482" " 86" " 92" " 88" " 68" " 75"
## [804,] "Binacle" " 306" " 42" " 52" " 67" " 39" " 56"
## [805,] "Barbaracle" " 500" " 72" "105" "115" " 54" " 86"
## [806,] "Skrelp" " 320" " 50" " 60" " 60" " 60" " 60"
## [807,] "Dragalge" " 494" " 65" " 75" " 90" " 97" "123"
## [808,] "Clauncher" " 330" " 50" " 53" " 62" " 58" " 63"
## [809,] "Clawitzer" " 500" " 71" " 73" " 88" "120" " 89"
## [810,] "Helioptile" " 289" " 44" " 38" " 33" " 61" " 43"
## [811,] "Heliolisk" " 481" " 62" " 55" " 52" "109" " 94"
## [812,] "Tyrunt" " 362" " 58" " 89" " 77" " 45" " 45"
## [813,] "Tyrantrum" " 521" " 82" "121" "119" " 69" " 59"
## [814,] "Amaura" " 362" " 77" " 59" " 50" " 67" " 63"
## [815,] "Aurorus" " 521" "123" " 77" " 72" " 99" " 92"
## [816,] "Sylveon" " 525" " 95" " 65" " 65" "110" "130"
## [817,] "Hawlucha" " 500" " 78" " 92" " 75" " 74" " 63"
## [818,] "Dedenne" " 431" " 67" " 58" " 57" " 81" " 67"
## [819,] "Carbink" " 500" " 50" " 50" "150" " 50" "150"
## [820,] "Goomy" " 300" " 45" " 50" " 35" " 55" " 75"
## [821,] "Sliggoo" " 452" " 68" " 75" " 53" " 83" "113"
## [822,] "Goodra" " 600" " 90" "100" " 70" "110" "150"
## [823,] "Klefki" " 470" " 57" " 80" " 91" " 80" " 87"
## [824,] "Phantump" " 309" " 43" " 70" " 48" " 50" " 60"
## [825,] "Trevenant" " 474" " 85" "110" " 76" " 65" " 82"
## [826,] "Pumpkaboo" " 335" " 49" " 66" " 70" " 44" " 55"
## [827,] "Mega Pumpkaboo" " 335" " 44" " 66" " 70" " 44" " 55"
## [828,] "Mega Pumpkaboo X" " 335" " 54" " 66" " 70" " 44" " 55"
## [829,] "Mega Pumpkaboo X" " 335" " 59" " 66" " 70" " 44" " 55"
## [830,] "Gourgeist" " 494" " 65" " 90" "122" " 58" " 75"
## [831,] "Mega Gourgeist" " 494" " 55" " 85" "122" " 58" " 75"
## [832,] "Mega Gourgeist X" " 494" " 75" " 95" "122" " 58" " 75"
## [833,] "Mega Gourgeist X" " 494" " 85" "100" "122" " 58" " 75"
## [834,] "Bergmite" " 304" " 55" " 69" " 85" " 32" " 35"
## [835,] "Avalugg" " 514" " 95" "117" "184" " 44" " 46"
## [836,] "Noibat" " 245" " 40" " 30" " 35" " 45" " 40"
## [837,] "Noivern" " 535" " 85" " 70" " 80" " 97" " 80"
## [838,] "Xerneas" " 680" "126" "131" " 95" "131" " 98"
## [839,] "Yveltal" " 680" "126" "131" " 95" "131" " 98"
## [840,] "Zygarde" " 600" "108" "100" "121" " 81" " 95"
## [841,] "Mega Zygarde" " 486" " 54" "100" " 71" " 61" " 85"
## [842,] "Mega Zygarde X" " 708" "216" "100" "121" " 91" " 95"
## [843,] "Diancie" " 600" " 50" "100" "150" "100" "150"
## [844,] "Mega Diancie" " 700" " 50" "160" "110" "160" "110"
## [845,] "Hoopa" " 600" " 80" "110" " 60" "150" "130"
## [846,] "Mega Hoopa" " 680" " 80" "160" " 60" "170" "130"
## [847,] "Volcanion" " 600" " 80" "110" "120" "130" " 90"
## [848,] "Rowlet" " 320" " 68" " 55" " 55" " 50" " 50"
## [849,] "Dartrix" " 420" " 78" " 75" " 75" " 70" " 70"
## [850,] "Decidueye" " 530" " 78" "107" " 75" "100" "100"
## [851,] "Litten" " 320" " 45" " 65" " 40" " 60" " 40"
## [852,] "Torracat" " 420" " 65" " 85" " 50" " 80" " 50"
## [853,] "Incineroar" " 530" " 95" "115" " 90" " 80" " 90"
## [854,] "Popplio" " 320" " 50" " 54" " 54" " 66" " 56"
## [855,] "Brionne" " 420" " 60" " 69" " 69" " 91" " 81"
## [856,] "Primarina" " 530" " 80" " 74" " 74" "126" "116"
## [857,] "Pikipek" " 265" " 35" " 75" " 30" " 30" " 30"
## [858,] "Trumbeak" " 355" " 55" " 85" " 50" " 40" " 50"
## [859,] "Toucannon" " 485" " 80" "120" " 75" " 75" " 75"
## [860,] "Yungoos" " 253" " 48" " 70" " 30" " 30" " 30"
## [861,] "Gumshoos" " 418" " 88" "110" " 60" " 55" " 60"
## [862,] "Grubbin" " 300" " 47" " 62" " 45" " 55" " 45"
## [863,] "Charjabug" " 400" " 57" " 82" " 95" " 55" " 75"
## [864,] "Vikavolt" " 500" " 77" " 70" " 90" "145" " 75"
## [865,] "Crabrawler" " 338" " 47" " 82" " 57" " 42" " 47"
## [866,] "Crabominable" " 478" " 97" "132" " 77" " 62" " 67"
## [867,] "Oricorio" " 476" " 75" " 70" " 70" " 98" " 70"
## [868,] "Mega Oricorio" " 476" " 75" " 70" " 70" " 98" " 70"
## [869,] "Mega Oricorio X" " 476" " 75" " 70" " 70" " 98" " 70"
## [870,] "Mega Oricorio X" " 476" " 75" " 70" " 70" " 98" " 70"
## [871,] "Cutiefly" " 304" " 40" " 45" " 40" " 55" " 40"
## [872,] "Ribombee" " 464" " 60" " 55" " 60" " 95" " 70"
## [873,] "Rockruff" " 280" " 45" " 65" " 40" " 30" " 40"
## [874,] "Mega Rockruff" " 280" " 45" " 65" " 40" " 30" " 40"
## [875,] "Lycanroc" " 487" " 75" "115" " 65" " 55" " 65"
## [876,] "Mega Lycanroc" " 487" " 85" "115" " 75" " 55" " 75"
## [877,] "Mega Lycanroc X" " 487" " 75" "117" " 65" " 55" " 65"
## [878,] "Wishiwashi" " 175" " 45" " 20" " 20" " 25" " 25"
## [879,] "Mega Wishiwashi" " 620" " 45" "140" "130" "140" "135"
## [880,] "Mareanie" " 305" " 50" " 53" " 62" " 43" " 52"
## [881,] "Toxapex" " 495" " 50" " 63" "152" " 53" "142"
## [882,] "Mudbray" " 385" " 70" "100" " 70" " 45" " 55"
## [883,] "Mudsdale" " 500" "100" "125" "100" " 55" " 85"
## [884,] "Dewpider" " 269" " 38" " 40" " 52" " 40" " 72"
## [885,] "Araquanid" " 454" " 68" " 70" " 92" " 50" "132"
## [886,] "Fomantis" " 250" " 40" " 55" " 35" " 50" " 35"
## [887,] "Lurantis" " 480" " 70" "105" " 90" " 80" " 90"
## [888,] "Morelull" " 285" " 40" " 35" " 55" " 65" " 75"
## [889,] "Shiinotic" " 405" " 60" " 45" " 80" " 90" "100"
## [890,] "Salandit" " 320" " 48" " 44" " 40" " 71" " 40"
## [891,] "Salazzle" " 480" " 68" " 64" " 60" "111" " 60"
## [892,] "Stufful" " 340" " 70" " 75" " 50" " 45" " 50"
## [893,] "Bewear" " 500" "120" "125" " 80" " 55" " 60"
## [894,] "Bounsweet" " 210" " 42" " 30" " 38" " 30" " 38"
## [895,] "Steenee" " 290" " 52" " 40" " 48" " 40" " 48"
## [896,] "Tsareena" " 510" " 72" "120" " 98" " 50" " 98"
## [897,] "Comfey" " 485" " 51" " 52" " 90" " 82" "110"
## [898,] "Oranguru" " 490" " 90" " 60" " 80" " 90" "110"
## [899,] "Passimian" " 490" "100" "120" " 90" " 40" " 60"
## [900,] "Wimpod" " 230" " 25" " 35" " 40" " 20" " 30"
## [901,] "Golisopod" " 530" " 75" "125" "140" " 60" " 90"
## [902,] "Sandygast" " 320" " 55" " 55" " 80" " 70" " 45"
## [903,] "Palossand" " 480" " 85" " 75" "110" "100" " 75"
## [904,] "Pyukumuku" " 410" " 55" " 60" "130" " 30" "130"
## [905,] "Type: Null" " 534" " 95" " 95" " 95" " 95" " 95"
## [906,] "Silvally" " 570" " 95" " 95" " 95" " 95" " 95"
## [907,] "Minior" " 440" " 60" " 60" "100" " 60" "100"
## [908,] "Mega Minior" " 500" " 60" "100" " 60" "100" " 60"
## [909,] "Komala" " 480" " 65" "115" " 65" " 75" " 95"
## [910,] "Turtonator" " 485" " 60" " 78" "135" " 91" " 85"
## [911,] "Togedemaru" " 435" " 65" " 98" " 63" " 40" " 73"
## [912,] "Mimikyu" " 476" " 55" " 90" " 80" " 50" "105"
## [913,] "Bruxish" " 475" " 68" "105" " 70" " 70" " 70"
## [914,] "Drampa" " 485" " 78" " 60" " 85" "135" " 91"
## [915,] "Dhelmise" " 517" " 70" "131" "100" " 86" " 90"
## [916,] "Jangmo-o" " 300" " 45" " 55" " 65" " 45" " 45"
## [917,] "Hakamo-o" " 420" " 55" " 75" " 90" " 65" " 70"
## [918,] "Kommo-o" " 600" " 75" "110" "125" "100" "105"
## [919,] "Tapu Koko" " 570" " 70" "115" " 85" " 95" " 75"
## [920,] "Tapu Lele" " 570" " 70" " 85" " 75" "130" "115"
## [921,] "Tapu Bulu" " 570" " 70" "130" "115" " 85" " 95"
## [922,] "Tapu Fini" " 570" " 70" " 75" "115" " 95" "130"
## [923,] "Cosmog" " 200" " 43" " 29" " 31" " 29" " 31"
## [924,] "Cosmoem" " 400" " 43" " 29" "131" " 29" "131"
## [925,] "Solgaleo" " 680" "137" "137" "107" "113" " 89"
## [926,] "Lunala" " 680" "137" "113" " 89" "137" "107"
## [927,] "Nihilego" " 570" "109" " 53" " 47" "127" "131"
## [928,] "Buzzwole" " 570" "107" "139" "139" " 53" " 53"
## [929,] "Pheromosa" " 570" " 71" "137" " 37" "137" " 37"
## [930,] "Xurkitree" " 570" " 83" " 89" " 71" "173" " 71"
## [931,] "Celesteela" " 570" " 97" "101" "103" "107" "101"
## [932,] "Kartana" " 570" " 59" "181" "131" " 59" " 31"
## [933,] "Guzzlord" " 570" "223" "101" " 53" " 97" " 53"
## [934,] "Necrozma" " 600" " 97" "107" "101" "127" " 89"
## [935,] "Mega Necrozma" " 680" " 97" "157" "127" "113" "109"
## [936,] "Mega Necrozma X" " 680" " 97" "113" "109" "157" "127"
## [937,] "Mega Necrozma X" " 754" " 97" "167" " 97" "167" " 97"
## [938,] "Magearna" " 600" " 80" " 95" "115" "130" "115"
## [939,] "Marshadow" " 600" " 90" "125" " 80" " 90" " 90"
## [940,] "Poipole" " 420" " 67" " 73" " 67" " 73" " 67"
## [941,] "Naganadel" " 540" " 73" " 73" " 73" "127" " 73"
## [942,] "Stakataka" " 570" " 61" "131" "211" " 53" "101"
## [943,] "Blacephalon" " 570" " 53" "127" " 53" "151" " 79"
## [944,] "Zeraora" " 600" " 88" "112" " 75" "102" " 80"
## [945,] "Meltan" " 300" " 46" " 65" " 65" " 55" " 35"
## [946,] "Melmetal" " 600" "135" "143" "143" " 80" " 65"
## [947,] "Grookey" " 310" " 50" " 65" " 50" " 40" " 40"
## [948,] "Thwackey" " 420" " 70" " 85" " 70" " 55" " 60"
## [949,] "Rillaboom" " 530" "100" "125" " 90" " 60" " 70"
## [950,] "Scorbunny" " 310" " 50" " 71" " 40" " 40" " 40"
## [951,] "Raboot" " 420" " 65" " 86" " 60" " 55" " 60"
## [952,] "Cinderace" " 530" " 80" "116" " 75" " 65" " 75"
## [953,] "Sobble" " 310" " 50" " 40" " 40" " 70" " 40"
## [954,] "Drizzile" " 420" " 65" " 60" " 55" " 95" " 55"
## [955,] "Inteleon" " 530" " 70" " 85" " 65" "125" " 65"
## [956,] "Skwovet" " 275" " 70" " 55" " 55" " 35" " 35"
## [957,] "Greedent" " 460" "120" " 95" " 95" " 55" " 75"
## [958,] "Rookidee" " 245" " 38" " 47" " 35" " 33" " 35"
## [959,] "Corvisquire" " 365" " 68" " 67" " 55" " 43" " 55"
## [960,] "Corviknight" " 495" " 98" " 87" "105" " 53" " 85"
## [961,] "Blipbug" " 180" " 25" " 20" " 20" " 25" " 45"
## [962,] "Dottler" " 335" " 50" " 35" " 80" " 50" " 90"
## [963,] "Orbeetle" " 505" " 60" " 45" "110" " 80" "120"
## [964,] "Nickit" " 245" " 40" " 28" " 28" " 47" " 52"
## [965,] "Thievul" " 455" " 70" " 58" " 58" " 87" " 92"
## [966,] "Gossifleur" " 250" " 40" " 40" " 60" " 40" " 60"
## [967,] "Eldegoss" " 460" " 60" " 50" " 90" " 80" "120"
## [968,] "Wooloo" " 270" " 42" " 40" " 55" " 40" " 45"
## [969,] "Dubwool" " 490" " 72" " 80" "100" " 60" " 90"
## [970,] "Chewtle" " 284" " 50" " 64" " 50" " 38" " 38"
## [971,] "Drednaw" " 485" " 90" "115" " 90" " 48" " 68"
## [972,] "Yamper" " 270" " 59" " 45" " 50" " 40" " 50"
## [973,] "Boltund" " 490" " 69" " 90" " 60" " 90" " 60"
## [974,] "Rolycoly" " 240" " 30" " 40" " 50" " 40" " 50"
## [975,] "Carkol" " 410" " 80" " 60" " 90" " 60" " 70"
## [976,] "Coalossal" " 510" "110" " 80" "120" " 80" " 90"
## [977,] "Applin" " 260" " 40" " 40" " 80" " 40" " 40"
## [978,] "Flapple" " 485" " 70" "110" " 80" " 95" " 60"
## [979,] "Appletun" " 485" "110" " 85" " 80" "100" " 80"
## [980,] "Silicobra" " 315" " 52" " 57" " 75" " 35" " 50"
## [981,] "Sandaconda" " 510" " 72" "107" "125" " 65" " 70"
## [982,] "Cramorant" " 475" " 70" " 85" " 55" " 85" " 95"
## [983,] "Arrokuda" " 280" " 41" " 63" " 40" " 40" " 30"
## [984,] "Barraskewda" " 490" " 61" "123" " 60" " 60" " 50"
## [985,] "Toxel" " 242" " 40" " 38" " 35" " 54" " 35"
## [986,] "Toxtricity" " 502" " 75" " 98" " 70" "114" " 70"
## [987,] "Mega Toxtricity" " 502" " 75" " 98" " 70" "114" " 70"
## [988,] "Sizzlipede" " 305" " 50" " 65" " 45" " 50" " 50"
## [989,] "Centiskorch" " 525" "100" "115" " 65" " 90" " 90"
## [990,] "Clobbopus" " 310" " 50" " 68" " 60" " 50" " 50"
## [991,] "Grapploct" " 480" " 80" "118" " 90" " 70" " 80"
## [992,] "Sinistea" " 308" " 40" " 45" " 45" " 74" " 54"
## [993,] "Polteageist" " 508" " 60" " 65" " 65" "134" "114"
## [994,] "Hatenna" " 265" " 42" " 30" " 45" " 56" " 53"
## [995,] "Hattrem" " 370" " 57" " 40" " 65" " 86" " 73"
## [996,] "Hatterene" " 510" " 57" " 90" " 95" "136" "103"
## [997,] "Impidimp" " 265" " 45" " 45" " 30" " 55" " 40"
## [998,] "Morgrem" " 370" " 65" " 60" " 45" " 75" " 55"
## [999,] "Grimmsnarl" " 510" " 95" "120" " 65" " 95" " 75"
## [1000,] "Obstagoon" " 520" " 93" " 90" "101" " 60" " 81"
## [1001,] "Perrserker" " 440" " 70" "110" "100" " 50" " 60"
## [1002,] "Cursola" " 510" " 60" " 95" " 50" "145" "130"
## [1003,] "Sirfetch'd" " 507" " 62" "135" " 95" " 68" " 82"
## [1004,] "Mr. Rime" " 520" " 80" " 85" " 75" "110" "100"
## [1005,] "Runerigus" " 483" " 58" " 95" "145" " 50" "105"
## [1006,] "Milcery" " 270" " 45" " 40" " 40" " 50" " 61"
## [1007,] "Alcremie" " 495" " 65" " 60" " 75" "110" "121"
## [1008,] "Falinks" " 470" " 65" "100" "100" " 70" " 60"
## [1009,] "Pincurchin" " 435" " 48" "101" " 95" " 91" " 85"
## [1010,] "Snom" " 185" " 30" " 25" " 35" " 45" " 30"
## [1011,] "Frosmoth" " 475" " 70" " 65" " 60" "125" " 90"
## [1012,] "Stonjourner" " 470" "100" "125" "135" " 20" " 20"
## [1013,] "Eiscue" " 470" " 75" " 80" "110" " 65" " 90"
## [1014,] "Mega Eiscue" " 470" " 75" " 80" " 70" " 65" " 50"
## [1015,] "Indeedee" " 475" " 60" " 65" " 55" "105" " 95"
## [1016,] "Mega Indeedee" " 475" " 70" " 55" " 65" " 95" "105"
## [1017,] "Morpeko" " 436" " 58" " 95" " 58" " 70" " 58"
## [1018,] "Mega Morpeko" " 436" " 58" " 95" " 58" " 70" " 58"
## [1019,] "Cufant" " 330" " 72" " 80" " 49" " 40" " 49"
## [1020,] "Copperajah" " 500" "122" "130" " 69" " 80" " 69"
## [1021,] "Dracozolt" " 505" " 90" "100" " 90" " 80" " 70"
## [1022,] "Arctozolt" " 505" " 90" "100" " 90" " 90" " 80"
## [1023,] "Dracovish" " 505" " 90" " 90" "100" " 70" " 80"
## [1024,] "Arctovish" " 505" " 90" " 90" "100" " 80" " 90"
## [1025,] "Duraludon" " 535" " 70" " 95" "115" "120" " 50"
## [1026,] "Dreepy" " 270" " 28" " 60" " 30" " 40" " 30"
## [1027,] "Drakloak" " 410" " 68" " 80" " 50" " 60" " 50"
## [1028,] "Dragapult" " 600" " 88" "120" " 75" "100" " 75"
## [1029,] "Zacian" " 720" " 92" "170" "115" " 80" "115"
## [1030,] "Mega Zacian" " 670" " 92" "130" "115" " 80" "115"
## [1031,] "Zamazenta" " 720" " 92" "130" "145" " 80" "145"
## [1032,] "Mega Zamazenta" " 670" " 92" "130" "115" " 80" "115"
## [1033,] "Eternatus" " 690" "140" " 85" " 95" "145" " 95"
## [1034,] "Mega Eternatus" "1125" "255" "115" "250" "125" "250"
## [1035,] "Kubfu" " 385" " 60" " 90" " 60" " 53" " 50"
## [1036,] "Urshifu" " 550" "100" "130" "100" " 63" " 60"
## [1037,] "Mega Urshifu" " 550" "100" "130" "100" " 63" " 60"
## [1038,] "Zarude" " 600" "105" "120" "105" " 70" " 95"
## [1039,] "Regieleki" " 580" " 80" "100" " 50" "100" " 50"
## [1040,] "Regidrago" " 580" "200" "100" " 50" "100" " 50"
## [1041,] "Glastrier" " 580" "100" "145" "130" " 65" "110"
## [1042,] "Spectrier" " 580" "100" " 65" " 60" "145" " 80"
## [1043,] "Calyrex" " 500" "100" " 80" " 80" " 80" " 80"
## [1044,] "Mega Calyrex" " 680" "100" "165" "150" " 85" "130"
## [1045,] "Mega Calyrex X" " 680" "100" " 85" " 80" "165" "100"
## Speed
## [1,] " 45"
## [2,] " 60"
## [3,] " 80"
## [4,] " 80"
## [5,] " 65"
## [6,] " 80"
## [7,] "100"
## [8,] "100"
## [9,] "100"
## [10,] " 43"
## [11,] " 58"
## [12,] " 78"
## [13,] " 78"
## [14,] " 45"
## [15,] " 30"
## [16,] " 70"
## [17,] " 50"
## [18,] " 35"
## [19,] " 75"
## [20,] "145"
## [21,] " 56"
## [22,] " 71"
## [23,] "101"
## [24,] "121"
## [25,] " 72"
## [26,] " 72"
## [27,] " 97"
## [28,] " 77"
## [29,] " 70"
## [30,] "100"
## [31,] " 55"
## [32,] " 80"
## [33,] " 90"
## [34,] "120"
## [35,] "110"
## [36,] "110"
## [37,] " 40"
## [38,] " 40"
## [39,] " 65"
## [40,] " 65"
## [41,] " 41"
## [42,] " 56"
## [43,] " 76"
## [44,] " 50"
## [45,] " 65"
## [46,] " 85"
## [47,] " 35"
## [48,] " 60"
## [49,] " 65"
## [50,] " 65"
## [51,] "100"
## [52,] "109"
## [53,] " 20"
## [54,] " 45"
## [55,] " 55"
## [56,] " 90"
## [57,] " 30"
## [58,] " 40"
## [59,] " 50"
## [60,] " 25"
## [61,] " 30"
## [62,] " 45"
## [63,] " 90"
## [64,] " 95"
## [65,] " 90"
## [66,] "120"
## [67,] "110"
## [68,] " 90"
## [69,] " 90"
## [70,] " 40"
## [71,] "115"
## [72,] "115"
## [73,] " 55"
## [74,] " 85"
## [75,] " 70"
## [76,] " 95"
## [77,] " 60"
## [78,] " 95"
## [79,] " 90"
## [80,] " 90"
## [81,] " 70"
## [82,] " 90"
## [83,] "105"
## [84,] "120"
## [85,] "150"
## [86,] " 35"
## [87,] " 45"
## [88,] " 55"
## [89,] " 40"
## [90,] " 55"
## [91,] " 70"
## [92,] " 70"
## [93,] "100"
## [94,] " 20"
## [95,] " 20"
## [96,] " 35"
## [97,] " 35"
## [98,] " 45"
## [99,] " 45"
## [100,] " 90"
## [101,] " 90"
## [102,] "105"
## [103,] "105"
## [104,] " 15"
## [105,] " 15"
## [106,] " 30"
## [107,] " 30"
## [108,] " 30"
## [109,] " 45"
## [110,] " 70"
## [111,] " 60"
## [112,] " 55"
## [113,] " 75"
## [114,] "110"
## [115,] " 45"
## [116,] " 70"
## [117,] " 25"
## [118,] " 25"
## [119,] " 50"
## [120,] " 50"
## [121,] " 40"
## [122,] " 70"
## [123,] " 80"
## [124,] " 95"
## [125,] "110"
## [126,] "130"
## [127,] " 70"
## [128,] " 42"
## [129,] " 67"
## [130,] " 50"
## [131,] " 75"
## [132,] "100"
## [133,] "150"
## [134,] " 40"
## [135,] " 55"
## [136,] " 45"
## [137,] " 35"
## [138,] " 45"
## [139,] " 45"
## [140,] " 87"
## [141,] " 76"
## [142,] " 30"
## [143,] " 35"
## [144,] " 60"
## [145,] " 60"
## [146,] " 25"
## [147,] " 40"
## [148,] " 50"
## [149,] " 60"
## [150,] " 90"
## [151,] "100"
## [152,] " 60"
## [153,] " 85"
## [154,] " 63"
## [155,] " 68"
## [156,] " 85"
## [157,] "115"
## [158,] " 90"
## [159,] "100"
## [160,] "105"
## [161,] " 95"
## [162,] "105"
## [163,] " 93"
## [164,] " 85"
## [165,] "105"
## [166,] "110"
## [167,] " 80"
## [168,] " 81"
## [169,] " 81"
## [170,] " 60"
## [171,] " 48"
## [172,] " 55"
## [173,] " 75"
## [174,] " 65"
## [175,] "130"
## [176,] " 65"
## [177,] " 40"
## [178,] " 35"
## [179,] " 55"
## [180,] " 55"
## [181,] " 80"
## [182,] "130"
## [183,] "150"
## [184,] " 30"
## [185,] " 85"
## [186,] " 95"
## [187,] "100"
## [188,] "100"
## [189,] " 90"
## [190,] " 90"
## [191,] " 50"
## [192,] " 70"
## [193,] " 80"
## [194,] "130"
## [195,] "130"
## [196,] "140"
## [197,] "100"
## [198,] " 45"
## [199,] " 60"
## [200,] " 80"
## [201,] " 65"
## [202,] " 80"
## [203,] "100"
## [204,] " 43"
## [205,] " 58"
## [206,] " 78"
## [207,] " 20"
## [208,] " 90"
## [209,] " 50"
## [210,] " 70"
## [211,] " 55"
## [212,] " 85"
## [213,] " 30"
## [214,] " 40"
## [215,] "130"
## [216,] " 67"
## [217,] " 67"
## [218,] " 60"
## [219,] " 15"
## [220,] " 15"
## [221,] " 20"
## [222,] " 40"
## [223,] " 70"
## [224,] " 95"
## [225,] " 35"
## [226,] " 45"
## [227,] " 55"
## [228,] " 45"
## [229,] " 50"
## [230,] " 40"
## [231,] " 50"
## [232,] " 30"
## [233,] " 70"
## [234,] " 50"
## [235,] " 80"
## [236,] "110"
## [237,] " 85"
## [238,] " 30"
## [239,] " 30"
## [240,] " 95"
## [241,] " 15"
## [242,] " 35"
## [243,] "110"
## [244,] " 65"
## [245,] " 91"
## [246,] " 30"
## [247,] " 30"
## [248,] " 85"
## [249,] " 48"
## [250,] " 33"
## [251,] " 85"
## [252,] " 15"
## [253,] " 40"
## [254,] " 45"
## [255,] " 85"
## [256,] " 30"
## [257,] " 30"
## [258,] " 30"
## [259,] " 45"
## [260,] " 85"
## [261,] " 65"
## [262,] " 75"
## [263,] " 5"
## [264,] " 85"
## [265,] " 75"
## [266,] "115"
## [267,] " 40"
## [268,] " 55"
## [269,] " 20"
## [270,] " 30"
## [271,] " 50"
## [272,] " 50"
## [273,] " 35"
## [274,] " 30"
## [275,] " 65"
## [276,] " 45"
## [277,] " 75"
## [278,] " 70"
## [279,] " 70"
## [280,] " 65"
## [281,] " 95"
## [282,] "115"
## [283,] " 85"
## [284,] " 40"
## [285,] " 50"
## [286,] " 60"
## [287,] " 85"
## [288,] " 75"
## [289,] " 35"
## [290,] " 70"
## [291,] " 65"
## [292,] " 95"
## [293,] " 83"
## [294,] "100"
## [295,] " 55"
## [296,] "115"
## [297,] "100"
## [298,] " 85"
## [299,] " 41"
## [300,] " 51"
## [301,] " 61"
## [302,] " 71"
## [303,] "110"
## [304,] " 90"
## [305,] "100"
## [306,] " 70"
## [307,] " 95"
## [308,] "120"
## [309,] "145"
## [310,] " 45"
## [311,] " 55"
## [312,] " 80"
## [313,] "100"
## [314,] " 40"
## [315,] " 50"
## [316,] " 60"
## [317,] " 70"
## [318,] " 35"
## [319,] " 70"
## [320,] " 60"
## [321,] " 60"
## [322,] "100"
## [323,] "100"
## [324,] " 20"
## [325,] " 15"
## [326,] " 65"
## [327,] " 15"
## [328,] " 65"
## [329,] " 30"
## [330,] " 50"
## [331,] " 70"
## [332,] " 30"
## [333,] " 60"
## [334,] " 80"
## [335,] " 85"
## [336,] "125"
## [337,] " 85"
## [338,] " 65"
## [339,] " 40"
## [340,] " 50"
## [341,] " 80"
## [342,] "100"
## [343,] " 65"
## [344,] " 80"
## [345,] " 35"
## [346,] " 70"
## [347,] " 30"
## [348,] " 90"
## [349,] "100"
## [350,] " 40"
## [351,] "160"
## [352,] " 40"
## [353,] " 28"
## [354,] " 48"
## [355,] " 68"
## [356,] " 25"
## [357,] " 50"
## [358,] " 20"
## [359,] " 30"
## [360,] " 50"
## [361,] " 90"
## [362,] " 50"
## [363,] " 20"
## [364,] " 50"
## [365,] " 50"
## [366,] " 30"
## [367,] " 40"
## [368,] " 50"
## [369,] " 50"
## [370,] " 60"
## [371,] " 80"
## [372,] "100"
## [373,] " 65"
## [374,] "105"
## [375,] "135"
## [376,] " 95"
## [377,] " 95"
## [378,] " 85"
## [379,] " 85"
## [380,] " 65"
## [381,] " 40"
## [382,] " 55"
## [383,] " 65"
## [384,] " 95"
## [385,] "105"
## [386,] " 60"
## [387,] " 60"
## [388,] " 35"
## [389,] " 40"
## [390,] " 20"
## [391,] " 20"
## [392,] " 60"
## [393,] " 80"
## [394,] " 60"
## [395,] " 10"
## [396,] " 70"
## [397,] "100"
## [398,] " 35"
## [399,] " 55"
## [400,] " 50"
## [401,] " 80"
## [402,] " 80"
## [403,] " 90"
## [404,] " 65"
## [405,] " 70"
## [406,] " 70"
## [407,] " 60"
## [408,] " 60"
## [409,] " 35"
## [410,] " 55"
## [411,] " 55"
## [412,] " 75"
## [413,] " 23"
## [414,] " 43"
## [415,] " 75"
## [416,] " 45"
## [417,] " 80"
## [418,] " 81"
## [419,] " 70"
## [420,] " 70"
## [421,] " 70"
## [422,] " 70"
## [423,] " 40"
## [424,] " 45"
## [425,] " 65"
## [426,] " 75"
## [427,] " 25"
## [428,] " 25"
## [429,] " 51"
## [430,] " 65"
## [431,] " 75"
## [432,] "115"
## [433,] " 23"
## [434,] " 50"
## [435,] " 80"
## [436,] "100"
## [437,] " 25"
## [438,] " 45"
## [439,] " 65"
## [440,] " 32"
## [441,] " 52"
## [442,] " 52"
## [443,] " 55"
## [444,] " 97"
## [445,] " 50"
## [446,] " 50"
## [447,] "100"
## [448,] "120"
## [449,] " 30"
## [450,] " 50"
## [451,] " 70"
## [452,] "110"
## [453,] " 50"
## [454,] " 50"
## [455,] " 50"
## [456,] "110"
## [457,] "110"
## [458,] "110"
## [459,] "110"
## [460,] " 90"
## [461,] " 90"
## [462,] " 90"
## [463,] " 90"
## [464,] " 95"
## [465,] "115"
## [466,] "100"
## [467,] "150"
## [468,] "150"
## [469,] " 90"
## [470,] "180"
## [471,] " 31"
## [472,] " 36"
## [473,] " 56"
## [474,] " 61"
## [475,] " 81"
## [476,] "108"
## [477,] " 40"
## [478,] " 50"
## [479,] " 60"
## [480,] " 60"
## [481,] " 80"
## [482,] "100"
## [483,] " 31"
## [484,] " 71"
## [485,] " 25"
## [486,] " 65"
## [487,] " 45"
## [488,] " 60"
## [489,] " 70"
## [490,] " 55"
## [491,] " 90"
## [492,] " 58"
## [493,] " 58"
## [494,] " 30"
## [495,] " 30"
## [496,] " 36"
## [497,] " 36"
## [498,] " 36"
## [499,] " 36"
## [500,] " 66"
## [501,] " 70"
## [502,] " 40"
## [503,] " 95"
## [504,] " 85"
## [505,] "115"
## [506,] " 35"
## [507,] " 85"
## [508,] " 34"
## [509,] " 39"
## [510,] "115"
## [511,] " 70"
## [512,] " 80"
## [513,] " 85"
## [514,] "105"
## [515,] "135"
## [516,] "105"
## [517,] " 71"
## [518,] " 85"
## [519,] "112"
## [520,] " 45"
## [521,] " 74"
## [522,] " 84"
## [523,] " 23"
## [524,] " 33"
## [525,] " 10"
## [526,] " 60"
## [527,] " 30"
## [528,] " 91"
## [529,] " 35"
## [530,] " 42"
## [531,] " 82"
## [532,] "102"
## [533,] " 92"
## [534,] " 5"
## [535,] " 60"
## [536,] " 90"
## [537,] "112"
## [538,] " 32"
## [539,] " 47"
## [540,] " 65"
## [541,] " 95"
## [542,] " 50"
## [543,] " 85"
## [544,] " 46"
## [545,] " 66"
## [546,] " 91"
## [547,] " 50"
## [548,] " 40"
## [549,] " 60"
## [550,] " 30"
## [551,] "125"
## [552,] " 60"
## [553,] " 50"
## [554,] " 40"
## [555,] " 50"
## [556,] " 95"
## [557,] " 83"
## [558,] " 80"
## [559,] " 95"
## [560,] " 95"
## [561,] " 65"
## [562,] " 95"
## [563,] " 80"
## [564,] " 90"
## [565,] " 80"
## [566,] "110"
## [567,] " 40"
## [568,] " 45"
## [569,] "110"
## [570,] " 91"
## [571,] " 86"
## [572,] " 86"
## [573,] " 86"
## [574,] " 86"
## [575,] " 86"
## [576,] " 95"
## [577,] " 80"
## [578,] "115"
## [579,] " 90"
## [580,] "100"
## [581,] " 77"
## [582,] "100"
## [583,] " 90"
## [584,] " 90"
## [585,] " 85"
## [586,] " 80"
## [587,] "100"
## [588,] "125"
## [589,] "100"
## [590,] "127"
## [591,] "120"
## [592,] "100"
## [593,] " 63"
## [594,] " 83"
## [595,] "113"
## [596,] " 45"
## [597,] " 55"
## [598,] " 65"
## [599,] " 45"
## [600,] " 60"
## [601,] " 70"
## [602,] " 42"
## [603,] " 77"
## [604,] " 55"
## [605,] " 60"
## [606,] " 80"
## [607,] " 66"
## [608,] "106"
## [609,] " 64"
## [610,] "101"
## [611,] " 64"
## [612,] "101"
## [613,] " 64"
## [614,] "101"
## [615,] " 24"
## [616,] " 29"
## [617,] " 43"
## [618,] " 65"
## [619,] " 93"
## [620,] " 76"
## [621,] "116"
## [622,] " 15"
## [623,] " 20"
## [624,] " 25"
## [625,] " 72"
## [626,] "114"
## [627,] " 68"
## [628,] " 88"
## [629,] " 50"
## [630,] " 50"
## [631,] " 35"
## [632,] " 40"
## [633,] " 45"
## [634,] " 64"
## [635,] " 69"
## [636,] " 74"
## [637,] " 45"
## [638,] " 85"
## [639,] " 42"
## [640,] " 42"
## [641,] " 92"
## [642,] " 57"
## [643,] " 47"
## [644,] "112"
## [645,] " 66"
## [646,] "116"
## [647,] " 30"
## [648,] " 90"
## [649,] " 98"
## [650,] " 98"
## [651,] " 65"
## [652,] " 74"
## [653,] " 92"
## [654,] " 50"
## [655,] " 50"
## [656,] " 95"
## [657,] " 55"
## [658,] " 95"
## [659,] "135"
## [660,] " 60"
## [661,] " 55"
## [662,] " 45"
## [663,] " 48"
## [664,] " 58"
## [665,] " 97"
## [666,] " 30"
## [667,] " 30"
## [668,] " 30"
## [669,] " 22"
## [670,] " 32"
## [671,] " 70"
## [672,] "110"
## [673,] " 65"
## [674,] " 75"
## [675,] " 65"
## [676,] "105"
## [677,] " 75"
## [678,] "115"
## [679,] " 45"
## [680,] " 55"
## [681,] " 65"
## [682,] " 20"
## [683,] " 30"
## [684,] " 30"
## [685,] " 55"
## [686,] " 98"
## [687,] " 44"
## [688,] " 59"
## [689,] " 79"
## [690,] " 75"
## [691,] " 95"
## [692,] "103"
## [693,] " 60"
## [694,] " 20"
## [695,] " 15"
## [696,] " 30"
## [697,] " 40"
## [698,] " 60"
## [699,] " 65"
## [700,] " 65"
## [701,] "108"
## [702,] " 10"
## [703,] " 20"
## [704,] " 30"
## [705,] " 50"
## [706,] " 90"
## [707,] " 60"
## [708,] " 40"
## [709,] " 50"
## [710,] " 30"
## [711,] " 40"
## [712,] " 20"
## [713,] " 55"
## [714,] " 80"
## [715,] " 57"
## [716,] " 67"
## [717,] " 97"
## [718,] " 40"
## [719,] " 50"
## [720,] "105"
## [721,] " 25"
## [722,] "145"
## [723,] " 32"
## [724,] " 32"
## [725,] " 65"
## [726,] "105"
## [727,] " 48"
## [728,] " 35"
## [729,] " 55"
## [730,] " 60"
## [731,] " 70"
## [732,] " 55"
## [733,] " 60"
## [734,] " 80"
## [735,] " 60"
## [736,] " 80"
## [737,] " 65"
## [738,] "109"
## [739,] " 38"
## [740,] " 58"
## [741,] " 98"
## [742,] " 60"
## [743,] "100"
## [744,] "108"
## [745,] "108"
## [746,] "108"
## [747,] "111"
## [748,] "121"
## [749,] "111"
## [750,] "101"
## [751,] " 90"
## [752,] " 90"
## [753,] "101"
## [754,] " 91"
## [755,] " 95"
## [756,] " 95"
## [757,] " 95"
## [758,] "108"
## [759,] "108"
## [760,] " 90"
## [761,] "128"
## [762,] " 99"
## [763,] " 38"
## [764,] " 57"
## [765,] " 64"
## [766,] " 60"
## [767,] " 73"
## [768,] "104"
## [769,] " 71"
## [770,] " 97"
## [771,] "122"
## [772,] "132"
## [773,] " 57"
## [774,] " 78"
## [775,] " 62"
## [776,] " 84"
## [777,] "126"
## [778,] " 35"
## [779,] " 29"
## [780,] " 89"
## [781,] " 72"
## [782,] "106"
## [783,] " 42"
## [784,] " 52"
## [785,] " 75"
## [786,] " 52"
## [787,] " 68"
## [788,] " 43"
## [789,] " 58"
## [790,] "102"
## [791,] " 68"
## [792,] "104"
## [793,] "104"
## [794,] " 28"
## [795,] " 35"
## [796,] " 60"
## [797,] " 60"
## [798,] " 23"
## [799,] " 29"
## [800,] " 49"
## [801,] " 72"
## [802,] " 45"
## [803,] " 73"
## [804,] " 50"
## [805,] " 68"
## [806,] " 30"
## [807,] " 44"
## [808,] " 44"
## [809,] " 59"
## [810,] " 70"
## [811,] "109"
## [812,] " 48"
## [813,] " 71"
## [814,] " 46"
## [815,] " 58"
## [816,] " 60"
## [817,] "118"
## [818,] "101"
## [819,] " 50"
## [820,] " 40"
## [821,] " 60"
## [822,] " 80"
## [823,] " 75"
## [824,] " 38"
## [825,] " 56"
## [826,] " 51"
## [827,] " 56"
## [828,] " 46"
## [829,] " 41"
## [830,] " 84"
## [831,] " 99"
## [832,] " 69"
## [833,] " 54"
## [834,] " 28"
## [835,] " 28"
## [836,] " 55"
## [837,] "123"
## [838,] " 99"
## [839,] " 99"
## [840,] " 95"
## [841,] "115"
## [842,] " 85"
## [843,] " 50"
## [844,] "110"
## [845,] " 70"
## [846,] " 80"
## [847,] " 70"
## [848,] " 42"
## [849,] " 52"
## [850,] " 70"
## [851,] " 70"
## [852,] " 90"
## [853,] " 60"
## [854,] " 40"
## [855,] " 50"
## [856,] " 60"
## [857,] " 65"
## [858,] " 75"
## [859,] " 60"
## [860,] " 45"
## [861,] " 45"
## [862,] " 46"
## [863,] " 36"
## [864,] " 43"
## [865,] " 63"
## [866,] " 43"
## [867,] " 93"
## [868,] " 93"
## [869,] " 93"
## [870,] " 93"
## [871,] " 84"
## [872,] "124"
## [873,] " 60"
## [874,] " 60"
## [875,] "112"
## [876,] " 82"
## [877,] "110"
## [878,] " 40"
## [879,] " 30"
## [880,] " 45"
## [881,] " 35"
## [882,] " 45"
## [883,] " 35"
## [884,] " 27"
## [885,] " 42"
## [886,] " 35"
## [887,] " 45"
## [888,] " 15"
## [889,] " 30"
## [890,] " 77"
## [891,] "117"
## [892,] " 50"
## [893,] " 60"
## [894,] " 32"
## [895,] " 62"
## [896,] " 72"
## [897,] "100"
## [898,] " 60"
## [899,] " 80"
## [900,] " 80"
## [901,] " 40"
## [902,] " 15"
## [903,] " 35"
## [904,] " 5"
## [905,] " 59"
## [906,] " 95"
## [907,] " 60"
## [908,] "120"
## [909,] " 65"
## [910,] " 36"
## [911,] " 96"
## [912,] " 96"
## [913,] " 92"
## [914,] " 36"
## [915,] " 40"
## [916,] " 45"
## [917,] " 65"
## [918,] " 85"
## [919,] "130"
## [920,] " 95"
## [921,] " 75"
## [922,] " 85"
## [923,] " 37"
## [924,] " 37"
## [925,] " 97"
## [926,] " 97"
## [927,] "103"
## [928,] " 79"
## [929,] "151"
## [930,] " 83"
## [931,] " 61"
## [932,] "109"
## [933,] " 43"
## [934,] " 79"
## [935,] " 77"
## [936,] " 77"
## [937,] "129"
## [938,] " 65"
## [939,] "125"
## [940,] " 73"
## [941,] "121"
## [942,] " 13"
## [943,] "107"
## [944,] "143"
## [945,] " 34"
## [946,] " 34"
## [947,] " 65"
## [948,] " 80"
## [949,] " 85"
## [950,] " 69"
## [951,] " 94"
## [952,] "119"
## [953,] " 70"
## [954,] " 90"
## [955,] "120"
## [956,] " 25"
## [957,] " 20"
## [958,] " 57"
## [959,] " 77"
## [960,] " 67"
## [961,] " 45"
## [962,] " 30"
## [963,] " 90"
## [964,] " 50"
## [965,] " 90"
## [966,] " 10"
## [967,] " 60"
## [968,] " 48"
## [969,] " 88"
## [970,] " 44"
## [971,] " 74"
## [972,] " 26"
## [973,] "121"
## [974,] " 30"
## [975,] " 50"
## [976,] " 30"
## [977,] " 20"
## [978,] " 70"
## [979,] " 30"
## [980,] " 46"
## [981,] " 71"
## [982,] " 85"
## [983,] " 66"
## [984,] "136"
## [985,] " 40"
## [986,] " 75"
## [987,] " 75"
## [988,] " 45"
## [989,] " 65"
## [990,] " 32"
## [991,] " 42"
## [992,] " 50"
## [993,] " 70"
## [994,] " 39"
## [995,] " 49"
## [996,] " 29"
## [997,] " 50"
## [998,] " 70"
## [999,] " 60"
## [1000,] " 95"
## [1001,] " 50"
## [1002,] " 30"
## [1003,] " 65"
## [1004,] " 70"
## [1005,] " 30"
## [1006,] " 34"
## [1007,] " 64"
## [1008,] " 75"
## [1009,] " 15"
## [1010,] " 20"
## [1011,] " 65"
## [1012,] " 70"
## [1013,] " 50"
## [1014,] "130"
## [1015,] " 95"
## [1016,] " 85"
## [1017,] " 97"
## [1018,] " 97"
## [1019,] " 40"
## [1020,] " 30"
## [1021,] " 75"
## [1022,] " 55"
## [1023,] " 75"
## [1024,] " 55"
## [1025,] " 85"
## [1026,] " 82"
## [1027,] "102"
## [1028,] "142"
## [1029,] "148"
## [1030,] "138"
## [1031,] "128"
## [1032,] "138"
## [1033,] "130"
## [1034,] "130"
## [1035,] " 72"
## [1036,] " 97"
## [1037,] " 97"
## [1038,] "105"
## [1039,] "200"
## [1040,] " 80"
## [1041,] " 30"
## [1042,] "130"
## [1043,] " 80"
## [1044,] " 50"
## [1045,] "150"plot(d2$HP)
plot(d2$Total)
plot(d2$Attack)
plot(d2$Defence)
plot(d2$Sp_attack)
plot(d2$Sp_defence)
plot(d2$Speed)
plot(md)
stem(d2$HP)##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 100
## 2 | 0000005555880000000000000000155555555555555555678888888888899
## 4 | 00000000000000000000000000000000000000000000000011112222333334444444+204
## 6 | 00000000000000000000000000000000000000000000000000000000000000000000+299
## 8 | 00000000000000000000000000000000000000000000000000000012233334455555+107
## 10 | 00000000000000000000000000000000000000000000133345555555555555566666+13
## 12 | 000002335555660005577
## 14 | 0040000
## 16 | 050
## 18 | 0
## 20 | 06
## 22 | 3
## 24 | 055stem(d2$Total)##
## The decimal point is 2 digit(s) to the right of the |
##
## 1 | 888999
## 2 | 00000000111111111122222223444444444
## 2 | 55555555555555555556666666667777777777777777788888888888888888888999+8
## 3 | 00000000000000000000000000000000000000011111111111111111111111111111+86
## 3 | 55555555555555566666666666666666777777777777788888888899999999999999
## 4 | 00000000000000111111111111111111111111111111111111111111111112222222+47
## 4 | 55555555555555555566666666666666666666666666666666667777777777777777+114
## 5 | 00000000000000000000000000000000000000000000000000000000000011111111+109
## 5 | 55555566667777777777777777888888888888888888888888888888889999
## 6 | 000000000000000000000000000000000000000000000011222233333333444
## 6 | 677777788888888888888888889
## 7 | 0000000001222
## 7 | 577888
## 8 |
## 8 |
## 9 |
## 9 |
## 10 |
## 10 |
## 11 | 3stem(d2$Attack)##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 55
## 1 | 0005
## 2 | 00000000002345555555578999
## 3 | 0000000000000000000000035555555555555555568888
## 4 | 00000000000000000000000000000011123445555555555555555555555555555555+7
## 5 | 00000000000000000000000000000000000000112222222233333334455555555555+32
## 6 | 00000000000000000000000000000000000000000001122222333333334444444555+57
## 7 | 00000000000000000000000000000000000000000011122222223333333445555555+35
## 8 | 00000000000000000000000000000000000000000000000111122222222333344444+45
## 9 | 00000000000000000000000000000000000000001222222223344555555555555555+17
## 10 | 00000000000000000000000000000000000000000000000000000011123334445555+14
## 11 | 0000000000000000000002223355555555555555555677778
## 12 | 00000000000000000000000000001333344555555555555555555555789
## 13 | 00000000000000000000001111224455555556779
## 14 | 00000000035555557
## 15 | 0000000000557
## 16 | 000000455557
## 17 | 000
## 18 | 00015
## 19 | 0stem(d2$Defence)##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 55
## 1 | 05555
## 2 | 00000035588
## 3 | 000000000000000000122344455555555555555555555555555555777788899
## 4 | 00000000000000000000000000000000000000000000000111222333333444455555+39
## 5 | 00000000000000000000000000000000000000000000000000000000000000001222+59
## 6 | 00000000000000000000000000000000000000000000000000000000000011222222+71
## 7 | 00000000000000000000000000000000000000000000000000000000000000000000+64
## 8 | 00000000000000000000000000000000000000000000000000000233344555555555+24
## 9 | 00000000000000000000000000000000000000000000000000011245555555555555+16
## 10 | 00000000000000000000000000000000000000000000011233555555555555555556
## 11 | 0000000000000001255555555555555555556889
## 12 | 00000000000000001122222355555555679
## 13 | 00000000000000111355559
## 14 | 0000000035555
## 15 | 00000002
## 16 | 0008
## 17 |
## 18 | 0004
## 19 |
## 20 | 00
## 21 | 1
## 22 |
## 23 | 000
## 24 |
## 25 | 0stem(d2$Sp_attack)##
## The decimal point is 1 digit(s) to the right of the |
##
## 1 | 000055555
## 2 | 000000000034455555555555555577999
## 3 | 00000000000000000000000000000000000122335555555555555555555555555555
## 4 | 00000000000000000000000000000000000000000000000000000000000000000000+53
## 5 | 00000000000000000000000000000000000000000000000000000013333333334444+58
## 6 | 00000000000000000000000000000000000000000000000000000000000011111112+67
## 7 | 00000000000000000000000000000000000000000001112223333344444455555555+13
## 8 | 00000000000000000000000000000000000000000011111112333333335555555555+17
## 9 | 00000000000000000000000000000111112222344555555555555555555555555555+13
## 10 | 00000000000000000000000000000000000000233455555555555555555555567789
## 11 | 000000000000000000011223344445555555556
## 12 | 00000000000000025555555555555555556777899
## 13 | 0000000000000112455555555677
## 14 | 00000055555555
## 15 | 00000000134479
## 16 | 005557
## 17 | 00035
## 18 | 000
## 19 | 4stem(d2$Sp_defence)##
## The decimal point is 1 digit(s) to the right of the |
##
## 2 | 00000003555555555555
## 3 | 00000000000000000000000000011123455555555555555555555555556777788899
## 4 | 00000000000000000000000000000000000000000001111122222333444555555555+38
## 5 | 00000000000000000000000000000000000000000000000000000000000000000001+73
## 6 | 00000000000000000000000000000000000000000000000000000000011111122223+63
## 7 | 00000000000000000000000000000000000000000000000000000000000001111112+57
## 8 | 00000000000000000000000000000000000000000000000000000000000001111122+46
## 9 | 00000000000000000000000000000000000000000000000000012245555555555555+22
## 10 | 00000000000000000000000000000000000001112355555555555555555555556777
## 11 | 000000000000000000034555555555555555666
## 12 | 000000000000000135556789
## 13 | 0000000000000112555558
## 14 | 00025
## 15 | 000000444
## 16 | 00
## 17 |
## 18 |
## 19 |
## 20 | 0
## 21 |
## 22 |
## 23 | 0
## 24 |
## 25 | 0stem(d2$Speed)##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 555
## 1 | 000035555555555555
## 2 | 00000000000000000002333345555555555556788889999
## 3 | 00000000000000000000000000000000000000000000000001122222223344445555+26
## 4 | 00000000000000000000000000000000000000001112222222223333333344445555+40
## 5 | 00000000000000000000000000000000000000000000000000000000011122222455+40
## 6 | 00000000000000000000000000000000000000000000000000000001112233344444+54
## 7 | 00000000000000000000000000000000000000000000000111111122222223334444+29
## 8 | 00000000000000000000000000000000000000011112223333444455555555555555+17
## 9 | 00000000000000000000000000000000000000001111122223333334555555555555+30
## 10 | 00000000000000000000000000000000001111111222334445555555555555566788
## 11 | 000000000000000000011222234555555555555566789
## 12 | 000000001111234555567889
## 13 | 000000000002555688
## 14 | 0235558
## 15 | 0000001
## 16 | 0
## 17 |
## 18 | 0
## 19 |
## 20 | 0stem(d2$HP, scale =2)##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 1
## 1 | 00
## 2 | 000000555588
## 3 | 0000000000000000155555555555555555678888888888899
## 4 | 00000000000000000000000000000000000000000000000011112222333334444444+49
## 5 | 00000000000000000000000000000000000000000000000000000000000000000000+75
## 6 | 00000000000000000000000000000000000000000000000000000000000000000000+109
## 7 | 00000000000000000000000000000000000000000000000000000000000000000000+110
## 8 | 00000000000000000000000000000000000000000000000000000012233334455555+16
## 9 | 00000000000000000000000000000000000000001111111222223555555555555555+11
## 10 | 00000000000000000000000000000000000000000000133345555555555555566666
## 11 | 00000000000145556
## 12 | 00000233555566
## 13 | 0005577
## 14 | 004
## 15 | 0000
## 16 | 05
## 17 | 0
## 18 |
## 19 | 0
## 20 | 0
## 21 | 6
## 22 | 3
## 23 |
## 24 |
## 25 | 055hist(d2$HP)
hist(d2$Total)
hist(d2$Attack)
hist(d2$Defence)
hist(d2$Sp_attack)
hist(d2$Sp_defence)
hist(d2$Speed)
hist(d2$HP, breaks = 'Sturges')
hist(d2$HP, breaks = 'Scott')
hist(d2$HP, breaks = 'FD')
hist(d2$HP, breaks = 'st')
hist(d2$HP, breaks = 'sc')
hist(d2$HP, breaks = 7)
hist(d2$HP, col = 'gray75', main = NULL, xlab = 'Size class of d', ylim = c(0,0.1), freq = FALSE)
dens = density(d2$HP)
dens##
## Call:
## density.default(x = d2$HP)
##
## Data: d2$HP (1045 obs.); Bandwidth 'bw' = 5.351
##
## x y
## Min. :-15.05 Min. :8.180e-07
## 1st Qu.: 56.47 1st Qu.:7.976e-05
## Median :128.00 Median :2.725e-04
## Mean :128.00 Mean :3.492e-03
## 3rd Qu.:199.53 3rd Qu.:5.412e-03
## Max. :271.05 Max. :1.765e-02The class() command to know the type of object.
class(d2)## [1] "data.frame"class(dimnames(d2))## [1] "list"class(d2$HP)## [1] "integer"The str() command to examine the structure of an object.
str(d2)## 'data.frame': 1045 obs. of 8 variables:
## $ Name : chr "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur" ...
## $ Total : int 318 405 525 625 309 405 534 634 634 314 ...
## $ HP : int 45 60 80 80 39 58 78 78 78 44 ...
## $ Attack : int 49 62 82 100 52 64 84 130 104 48 ...
## $ Defence : int 49 63 83 123 43 58 78 111 78 65 ...
## $ Sp_attack : int 65 80 100 122 60 80 109 130 159 50 ...
## $ Sp_defence: int 65 80 100 120 50 65 85 85 115 64 ...
## $ Speed : int 45 60 80 80 65 80 100 100 100 43 ...str(d2$HP)## int [1:1045] 45 60 80 80 39 58 78 78 78 44 ...structure(head(d2))ls.str(pattern = 'd2')## d2 : 'data.frame': 1045 obs. of 8 variables:
## $ Name : chr "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur" ...
## $ Total : int 318 405 525 625 309 405 534 634 634 314 ...
## $ HP : int 45 60 80 80 39 58 78 78 78 44 ...
## $ Attack : int 49 62 82 100 52 64 84 130 104 48 ...
## $ Defence : int 49 63 83 123 43 58 78 111 78 65 ...
## $ Sp_attack : int 65 80 100 122 60 80 109 130 159 50 ...
## $ Sp_defence: int 65 80 100 120 50 65 85 85 115 64 ...
## $ Speed : int 45 60 80 80 65 80 100 100 100 43 ...attach(head(d2))
stack(head(d2))dimnames(d2)[[1]]## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
## [11] "11" "12" "13" "14" "15" "16" "17" "18" "19" "20"
## [21] "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
## [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40"
## [41] "41" "42" "43" "44" "45" "46" "47" "48" "49" "50"
## [51] "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70"
## [71] "71" "72" "73" "74" "75" "76" "77" "78" "79" "80"
## [81] "81" "82" "83" "84" "85" "86" "87" "88" "89" "90"
## [91] "91" "92" "93" "94" "95" "96" "97" "98" "99" "100"
## [101] "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
## [111] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130"
## [131] "131" "132" "133" "134" "135" "136" "137" "138" "139" "140"
## [141] "141" "142" "143" "144" "145" "146" "147" "148" "149" "150"
## [151] "151" "152" "153" "154" "155" "156" "157" "158" "159" "160"
## [161] "161" "162" "163" "164" "165" "166" "167" "168" "169" "170"
## [171] "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190"
## [191] "191" "192" "193" "194" "195" "196" "197" "198" "199" "200"
## [201] "201" "202" "203" "204" "205" "206" "207" "208" "209" "210"
## [211] "211" "212" "213" "214" "215" "216" "217" "218" "219" "220"
## [221] "221" "222" "223" "224" "225" "226" "227" "228" "229" "230"
## [231] "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
## [241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250"
## [251] "251" "252" "253" "254" "255" "256" "257" "258" "259" "260"
## [261] "261" "262" "263" "264" "265" "266" "267" "268" "269" "270"
## [271] "271" "272" "273" "274" "275" "276" "277" "278" "279" "280"
## [281] "281" "282" "283" "284" "285" "286" "287" "288" "289" "290"
## [291] "291" "292" "293" "294" "295" "296" "297" "298" "299" "300"
## [301] "301" "302" "303" "304" "305" "306" "307" "308" "309" "310"
## [311] "311" "312" "313" "314" "315" "316" "317" "318" "319" "320"
## [321] "321" "322" "323" "324" "325" "326" "327" "328" "329" "330"
## [331] "331" "332" "333" "334" "335" "336" "337" "338" "339" "340"
## [341] "341" "342" "343" "344" "345" "346" "347" "348" "349" "350"
## [351] "351" "352" "353" "354" "355" "356" "357" "358" "359" "360"
## [361] "361" "362" "363" "364" "365" "366" "367" "368" "369" "370"
## [371] "371" "372" "373" "374" "375" "376" "377" "378" "379" "380"
## [381] "381" "382" "383" "384" "385" "386" "387" "388" "389" "390"
## [391] "391" "392" "393" "394" "395" "396" "397" "398" "399" "400"
## [401] "401" "402" "403" "404" "405" "406" "407" "408" "409" "410"
## [411] "411" "412" "413" "414" "415" "416" "417" "418" "419" "420"
## [421] "421" "422" "423" "424" "425" "426" "427" "428" "429" "430"
## [431] "431" "432" "433" "434" "435" "436" "437" "438" "439" "440"
## [441] "441" "442" "443" "444" "445" "446" "447" "448" "449" "450"
## [451] "451" "452" "453" "454" "455" "456" "457" "458" "459" "460"
## [461] "461" "462" "463" "464" "465" "466" "467" "468" "469" "470"
## [471] "471" "472" "473" "474" "475" "476" "477" "478" "479" "480"
## [481] "481" "482" "483" "484" "485" "486" "487" "488" "489" "490"
## [491] "491" "492" "493" "494" "495" "496" "497" "498" "499" "500"
## [501] "501" "502" "503" "504" "505" "506" "507" "508" "509" "510"
## [511] "511" "512" "513" "514" "515" "516" "517" "518" "519" "520"
## [521] "521" "522" "523" "524" "525" "526" "527" "528" "529" "530"
## [531] "531" "532" "533" "534" "535" "536" "537" "538" "539" "540"
## [541] "541" "542" "543" "544" "545" "546" "547" "548" "549" "550"
## [551] "551" "552" "553" "554" "555" "556" "557" "558" "559" "560"
## [561] "561" "562" "563" "564" "565" "566" "567" "568" "569" "570"
## [571] "571" "572" "573" "574" "575" "576" "577" "578" "579" "580"
## [581] "581" "582" "583" "584" "585" "586" "587" "588" "589" "590"
## [591] "591" "592" "593" "594" "595" "596" "597" "598" "599" "600"
## [601] "601" "602" "603" "604" "605" "606" "607" "608" "609" "610"
## [611] "611" "612" "613" "614" "615" "616" "617" "618" "619" "620"
## [621] "621" "622" "623" "624" "625" "626" "627" "628" "629" "630"
## [631] "631" "632" "633" "634" "635" "636" "637" "638" "639" "640"
## [641] "641" "642" "643" "644" "645" "646" "647" "648" "649" "650"
## [651] "651" "652" "653" "654" "655" "656" "657" "658" "659" "660"
## [661] "661" "662" "663" "664" "665" "666" "667" "668" "669" "670"
## [671] "671" "672" "673" "674" "675" "676" "677" "678" "679" "680"
## [681] "681" "682" "683" "684" "685" "686" "687" "688" "689" "690"
## [691] "691" "692" "693" "694" "695" "696" "697" "698" "699" "700"
## [701] "701" "702" "703" "704" "705" "706" "707" "708" "709" "710"
## [711] "711" "712" "713" "714" "715" "716" "717" "718" "719" "720"
## [721] "721" "722" "723" "724" "725" "726" "727" "728" "729" "730"
## [731] "731" "732" "733" "734" "735" "736" "737" "738" "739" "740"
## [741] "741" "742" "743" "744" "745" "746" "747" "748" "749" "750"
## [751] "751" "752" "753" "754" "755" "756" "757" "758" "759" "760"
## [761] "761" "762" "763" "764" "765" "766" "767" "768" "769" "770"
## [771] "771" "772" "773" "774" "775" "776" "777" "778" "779" "780"
## [781] "781" "782" "783" "784" "785" "786" "787" "788" "789" "790"
## [791] "791" "792" "793" "794" "795" "796" "797" "798" "799" "800"
## [801] "801" "802" "803" "804" "805" "806" "807" "808" "809" "810"
## [811] "811" "812" "813" "814" "815" "816" "817" "818" "819" "820"
## [821] "821" "822" "823" "824" "825" "826" "827" "828" "829" "830"
## [831] "831" "832" "833" "834" "835" "836" "837" "838" "839" "840"
## [841] "841" "842" "843" "844" "845" "846" "847" "848" "849" "850"
## [851] "851" "852" "853" "854" "855" "856" "857" "858" "859" "860"
## [861] "861" "862" "863" "864" "865" "866" "867" "868" "869" "870"
## [871] "871" "872" "873" "874" "875" "876" "877" "878" "879" "880"
## [881] "881" "882" "883" "884" "885" "886" "887" "888" "889" "890"
## [891] "891" "892" "893" "894" "895" "896" "897" "898" "899" "900"
## [901] "901" "902" "903" "904" "905" "906" "907" "908" "909" "910"
## [911] "911" "912" "913" "914" "915" "916" "917" "918" "919" "920"
## [921] "921" "922" "923" "924" "925" "926" "927" "928" "929" "930"
## [931] "931" "932" "933" "934" "935" "936" "937" "938" "939" "940"
## [941] "941" "942" "943" "944" "945" "946" "947" "948" "949" "950"
## [951] "951" "952" "953" "954" "955" "956" "957" "958" "959" "960"
## [961] "961" "962" "963" "964" "965" "966" "967" "968" "969" "970"
## [971] "971" "972" "973" "974" "975" "976" "977" "978" "979" "980"
## [981] "981" "982" "983" "984" "985" "986" "987" "988" "989" "990"
## [991] "991" "992" "993" "994" "995" "996" "997" "998" "999" "1000"
## [1001] "1001" "1002" "1003" "1004" "1005" "1006" "1007" "1008" "1009" "1010"
## [1011] "1011" "1012" "1013" "1014" "1015" "1016" "1017" "1018" "1019" "1020"
## [1021] "1021" "1022" "1023" "1024" "1025" "1026" "1027" "1028" "1029" "1030"
## [1031] "1031" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1040"
## [1041] "1041" "1042" "1043" "1044" "1045"dimnames(d2)[[2]]## [1] "Name" "Total" "HP" "Attack" "Defence"
## [6] "Sp_attack" "Sp_defence" "Speed"head(d2, n=10)Simple math:
search()## [1] ".GlobalEnv" "head(d2)" "package:stats"
## [4] "package:graphics" "package:grDevices" "package:utils"
## [7] "package:datasets" "package:methods" "Autoloads"
## [10] "package:base"sin(d2$HP)## [1] 0.85090352 -0.30481062 -0.99388865 -0.99388865 0.96379539 0.99287265
## [7] 0.51397846 0.51397846 0.51397846 0.01770193 0.63673801 -0.44411267
## [13] -0.44411267 0.85090352 -0.26237485 -0.30481062 0.74511316 0.85090352
## [19] 0.82682868 0.82682868 0.74511316 0.16735570 0.96836446 0.96836446
## [25] -0.98803162 -0.98803162 -0.99975517 -0.38778164 0.74511316 0.82682868
## [31] -0.42818267 -0.30481062 -0.42818267 0.85090352 -0.30481062 -0.30481062
## [37] -0.26237485 -0.26237485 -0.38778164 -0.38778164 -0.99975517 0.77389068
## [43] 0.89399666 0.90178835 -0.96611777 -0.62988799 0.77389068 0.68326171
## [49] 0.29636858 0.29636858 -0.67677196 -0.67677196 0.94543533 0.98023966
## [55] 0.74511316 -0.38778164 0.85090352 -0.30481062 -0.38778164 -0.42818267
## [61] -0.30481062 -0.30481062 0.77389068 -0.54402111 -0.54402111 -0.42818267
## [67] -0.42818267 0.74511316 0.74511316 -0.26237485 0.82682868 0.82682868
## [73] -0.26237485 -0.99388865 0.74511316 0.82682868 -0.99975517 0.89399666
## [79] 0.74511316 0.82682868 0.89399666 -0.13235175 0.74511316 -0.99975517
## [85] -0.99975517 0.77389068 -0.99388865 0.89399666 -0.26237485 0.82682868
## [91] -0.99388865 0.74511316 -0.99388865 0.74511316 0.74511316 -0.99975517
## [97] -0.99975517 -0.99388865 -0.99388865 -0.26237485 -0.26237485 0.82682868
## [103] 0.82682868 0.89399666 0.89399666 0.68326171 0.68326171 0.68326171
## [109] -0.13235175 -0.26237485 0.98662759 0.98662759 -0.42818267 -0.30481062
## [115] 0.82682868 0.89399666 -0.99388865 -0.99388865 -0.97053528 -0.97053528
## [121] -0.98803162 -0.26237485 -0.98803162 0.85090352 -0.30481062 -0.30481062
## [127] -0.42818267 -0.30481062 -0.17607562 -0.98803162 -0.99975517 0.74511316
## [133] -0.30481062 -0.30481062 0.68326171 0.68326171 -0.26237485 -0.30481062
## [139] -0.30481062 -0.26237485 -0.26237485 0.89399666 0.74511316 0.82682868
## [145] 0.82682868 -0.99388865 -0.97053528 -0.97052802 0.82682868 -0.97053528
## [151] -0.97053528 -0.98803162 -0.99975517 0.85090352 -0.99388865 -0.98803162
## [157] -0.30481062 0.74511316 -0.26237485 0.77389068 0.82682868 0.82682868
## [163] 0.82682868 0.82682868 0.82682868 -0.38778164 0.91294525 0.68326171
## [169] 0.68326171 -0.93010595 -0.76825466 -0.99975517 0.82682868 -0.93010595
## [175] 0.82682868 0.82682868 0.82682868 -0.42818267 0.77389068 -0.98803162
## [181] -0.30481062 -0.99388865 -0.99388865 0.21942526 0.89399666 0.89399666
## [187] 0.89399666 0.89399666 0.89399666 0.89399666 -0.15862267 -0.96611777
## [193] 0.10598751 -0.72714250 -0.72714250 -0.72714250 -0.50636564 0.85090352
## [199] -0.30481062 -0.99388865 0.96379539 0.99287265 0.51397846 -0.26237485
## [205] 0.82682868 -0.17607562 -0.42818267 -0.17607562 -0.30481062 -0.50636564
## [211] 0.74511316 -0.99975517 0.74511316 0.77389068 -0.17607562 -0.38778164
## [217] -0.61604046 0.91294525 -0.26237485 0.89399666 -0.42818267 -0.99975517
## [223] 0.74511316 0.82682868 -0.99975517 0.77389068 0.89399666 0.89399666
## [229] -0.38778164 0.77389068 -0.50636564 0.77389068 0.89399666 -0.42818267
## [235] -0.99975517 -0.38778164 -0.99975517 -0.98803162 -0.38778164 0.82682868
## [241] -0.99975517 0.68326171 0.82682868 0.68326171 -0.30481062 0.68326171
## [247] 0.68326171 -0.30481062 -0.76825466 0.99779928 0.77389068 -0.26237485
## [253] -0.38778164 -0.50636564 0.82682868 -0.38778164 -0.38778164 -0.30481062
## [259] 0.89399666 0.82682868 0.77389068 0.77389068 0.91294525 -0.99388865
## [265] -0.99388865 -0.99975517 -0.30481062 0.89399666 0.74511316 -0.30481062
## [271] -0.26237485 -0.50636564 0.82682868 -0.30481062 -0.42818267 -0.38778164
## [277] 0.85090352 -0.17607562 0.82682868 0.85090352 -0.38778164 -0.38778164
## [283] -0.38778164 0.89399666 0.89399666 -0.17607562 -0.67677196 -0.99975517
## [289] -0.42818267 -0.26237485 0.85090352 0.85090352 0.85090352 0.68326171
## [295] -0.50639163 0.89399666 0.94543533 -0.50636564 -0.26237485 0.77389068
## [301] -0.50636564 -0.50636564 -0.72714250 -0.72714250 -0.50636564 0.74511316
## [307] -0.26237485 0.77389068 0.77389068 0.85090352 -0.30481062 -0.99388865
## [313] -0.99388865 -0.26237485 0.77389068 -0.50636564 -0.50636564 -0.42818267
## [319] 0.77389068 0.29636858 0.29636858 0.51397846 0.51397846 0.85090352
## [325] -0.26237485 -0.30481062 -0.26237485 -0.30481062 0.74511316 -0.30481062
## [331] -0.99388865 0.74511316 0.77389068 0.89399666 0.74511316 -0.30481062
## [337] 0.74511316 -0.30481062 0.27090579 0.29636858 -0.89792768 -0.89792768
## [343] 0.74511316 0.77389068 -0.30481062 -0.30481062 -0.30481062 -0.99388865
## [349] -0.71487643 -0.40403765 -0.96611777 0.84147098 0.92002604 0.73319032
## [355] -0.32162240 0.25382336 -0.49102159 -0.26237485 -0.98803162 -0.26237485
## [361] 0.77389068 -0.26237485 -0.26237485 -0.26237485 -0.26237485 -0.26237485
## [367] -0.30481062 0.77389068 0.77389068 -0.98803162 -0.30481062 -0.30481062
## [373] 0.74511316 0.77389068 0.77389068 -0.30481062 -0.30481062 0.82682868
## [379] 0.82682868 -0.26237485 0.77389068 -0.50636564 0.85090352 0.77389068
## [385] 0.77389068 -0.93010595 0.34664946 -0.30481062 0.77389068 0.77389068
## [391] 0.77389068 -0.30481062 -0.99388865 -0.30481062 0.85090352 -0.26237485
## [397] -0.99388865 -0.26237485 0.77389068 0.85090352 -0.38778164 -0.38778164
## [403] -0.67677196 -0.67677196 0.89399666 0.89399666 -0.26237485 -0.04424268
## [409] -0.83177474 0.16735570 0.74511316 -0.30481062 -0.02655115 -0.92345845
## [415] 0.85090352 -0.38778164 0.91294525 0.68326171 0.77389068 0.77389068
## [421] 0.77389068 0.77389068 -0.30481062 0.01770193 0.92002604 0.92002604
## [427] 0.91294525 0.74511316 -0.99920683 -0.38778164 0.82682868 0.82682868
## [433] 0.68326171 -0.26237485 -0.99388865 -0.99388865 0.77389068 0.89399666
## [439] -0.04424268 -0.42818267 -0.99975517 -0.99975517 -0.50636564 -0.83177474
## [445] 0.85090352 0.82682868 0.68326171 0.68326171 0.74511316 -0.30481062
## [451] -0.99388865 -0.99388865 -0.99388865 -0.99388865 -0.99388865 -0.99388865
## [457] -0.99388865 -0.99388865 -0.99388865 -0.50636564 -0.50636564 -0.50636564
## [463] -0.50636564 -0.97053528 -0.97053528 -0.50636564 -0.26237485 -0.26237485
## [469] -0.26237485 -0.26237485 -0.99975517 -0.38778164 0.68326171 0.01770193
## [475] 0.92002604 0.56610764 0.39592515 0.92002604 0.73319032 0.74511316
## [481] -0.99975517 -0.17607562 0.63673801 -0.44411267 -0.64353813 0.99952016
## [487] 0.85090352 -0.30481062 -0.99388865 0.74511316 -0.30481062 -0.85551998
## [493] 0.37960774 -0.98803162 -0.30481062 0.74511316 -0.30481062 -0.30481062
## [499] -0.30481062 0.77389068 -0.98803162 0.77389068 -0.30481062 -0.99975517
## [505] -0.17607562 0.85090352 0.77389068 0.56610764 -0.86455145 -0.38778164
## [511] 0.89399666 -0.71487643 -0.99975517 0.82682868 0.82682868 -0.30481062
## [517] -0.50636564 -0.95375265 0.95105465 0.85090352 0.16735570 0.62298863
## [523] 0.43616476 -0.85551998 -0.26237485 0.91294525 -0.50636564 0.56610764
## [529] -0.26237485 0.99287265 -0.89792768 0.92681851 0.92681851 0.08836869
## [535] 0.74511316 0.77389068 0.77389068 -0.89792768 0.92681851 0.74511316
## [541] 0.77389068 -0.76825466 0.96836446 -0.98514626 -0.95375265 -0.11478481
## [547] 0.85090352 -0.30481062 0.89399666 0.89399666 0.77389068 0.77389068
## [553] -0.04424268 0.94543533 -0.50636564 -0.38778164 -0.38778164 -0.17607562
## [559] -0.92345845 0.82682868 0.82682868 -0.38778164 -0.04424268 -0.17607562
## [565] -0.89792768 -0.89792768 -0.30481062 0.85090352 0.77389068 -0.26237485
## [571] -0.26237485 -0.26237485 -0.26237485 -0.26237485 -0.26237485 -0.38778164
## [577] -0.99388865 -0.38778164 -0.50636564 0.89399666 0.10598751 -0.04424268
## [583] -0.71487643 -0.71487643 0.58061118 -0.99388865 -0.50636564 0.77389068
## [589] -0.50636564 -0.50636564 0.58061118 -0.50636564 0.85090352 -0.30481062
## [595] -0.38778164 0.82682868 0.89399666 -0.04424268 -0.99975517 -0.38778164
## [601] 0.68326171 0.85090352 -0.30481062 0.85090352 0.82682868 -0.17607562
## [607] -0.15862267 0.92002604 -0.26237485 -0.38778164 -0.26237485 -0.38778164
## [613] -0.26237485 -0.38778164 0.56610764 0.23666139 -0.26237485 -0.73918070
## [619] -0.99388865 0.85090352 -0.38778164 -0.99975517 0.77389068 -0.17607562
## [625] 0.82682868 -0.85551998 -0.30481062 -0.04424268 0.62298863 0.62298863
## [631] -0.38778164 -0.17607562 -0.97053528 -0.26237485 -0.38778164 -0.97053528
## [637] 0.58061118 -0.38778164 0.85090352 -0.99975517 -0.38778164 -0.98803162
## [643] 0.74511316 -0.30481062 0.74511316 -0.30481062 0.85090352 0.77389068
## [649] 0.77389068 0.77389068 -0.26237485 -0.30481062 0.68326171 0.77389068
## [655] 0.77389068 -0.97053528 -0.97053528 -0.97053528 -0.97053528 -0.38778164
## [661] -0.26237485 0.77389068 -0.26237485 0.82682868 0.25382336 0.29636858
## [667] 0.29636858 0.99287265 -0.55878905 -0.98514626 -0.99975517 -0.38778164
## [673] -0.26237485 -0.99388865 0.74511316 -0.30481062 -0.99975517 -0.38778164
## [679] 0.85090352 -0.30481062 0.77389068 0.85090352 0.82682868 -0.04424268
## [685] -0.73918070 -0.38778164 -0.99177885 0.67022918 0.95105465 -0.30481062
## [691] -0.99388865 -0.99975517 -0.26237485 0.77389068 -0.11478481 0.78498039
## [697] -0.99975517 -0.50636564 0.99779728 -0.26237485 0.77389068 0.01770193
## [703] -0.98514626 0.74511316 -0.30481062 -0.30481062 -0.42818267 0.82682868
## [709] -0.17607562 -0.99975517 -0.38778164 -0.26237485 -0.30481062 -0.30481062
## [715] 0.90178835 -0.02655115 0.56610764 -0.99975517 0.68326171 -0.99388865
## [721] -0.26237485 -0.99388865 0.81674261 0.81674261 0.85090352 0.82682868
## [727] 0.99952016 0.63673801 0.86006941 0.85090352 0.82682868 0.68326171
## [733] 0.77389068 -0.50636564 0.77389068 -0.04424268 -0.17607562 0.99287265
## [739] 0.98662759 0.25382336 -0.77946607 -0.99975517 -0.17607562 0.10598751
## [745] 0.10598751 0.10598751 -0.44411267 -0.44411267 -0.44411267 -0.44411267
## [751] -0.50636564 -0.50636564 0.86006941 0.86006941 -0.61604046 -0.61604046
## [757] -0.61604046 0.10598751 0.10598751 -0.50636564 -0.50636564 0.95105465
## [763] -0.52155100 -0.96611777 0.03539830 0.74511316 0.63673801 -0.38778164
## [769] -0.15862267 -0.55878905 0.25382336 0.25382336 0.29636858 -0.17607562
## [775] 0.85090352 -0.73918070 0.51397846 0.29636858 0.85090352 -0.99388865
## [781] -0.73918070 -0.92345845 0.01770193 -0.55878905 0.51397846 -0.02655115
## [787] -0.45990349 -0.85551998 0.68326171 -0.38778164 -0.73918070 -0.98514626
## [793] -0.98514626 0.85090352 0.63673801 -0.30481062 -0.30481062 0.51397846
## [799] 0.45202579 -0.73918070 0.31322878 0.39592515 -0.92345845 -0.91652155
## [805] 0.25382336 -0.26237485 0.82682868 -0.26237485 0.95105465 0.01770193
## [811] -0.73918070 0.99287265 0.31322878 0.99952016 -0.45990349 0.68326171
## [817] 0.51397846 -0.85551998 -0.26237485 0.85090352 -0.89792768 0.89399666
## [823] 0.43616476 -0.83177474 -0.17607562 -0.95375265 0.01770193 -0.55878905
## [829] 0.63673801 0.82682868 -0.99975517 -0.38778164 -0.17607562 -0.99975517
## [835] 0.68326171 0.74511316 -0.17607562 0.32999083 0.32999083 0.92681851
## [841] -0.55878905 0.69605849 -0.26237485 -0.26237485 -0.99388865 -0.99388865
## [847] -0.99388865 -0.89792768 0.51397846 0.51397846 0.85090352 0.82682868
## [853] 0.68326171 -0.26237485 -0.30481062 -0.99388865 -0.42818267 -0.99975517
## [859] -0.99388865 -0.76825466 0.03539830 0.12357312 0.43616476 0.99952016
## [865] 0.12357312 0.37960774 -0.38778164 -0.38778164 -0.38778164 -0.38778164
## [871] 0.74511316 -0.30481062 0.85090352 0.85090352 -0.38778164 -0.17607562
## [877] -0.38778164 0.85090352 0.85090352 -0.26237485 -0.26237485 0.77389068
## [883] -0.50636564 0.29636858 -0.89792768 0.74511316 0.77389068 0.74511316
## [889] -0.30481062 -0.76825466 -0.89792768 0.77389068 0.58061118 -0.91652155
## [895] 0.98662759 0.25382336 0.67022918 0.89399666 -0.50636564 -0.13235175
## [901] -0.38778164 -0.99975517 -0.17607562 -0.99975517 0.68326171 0.68326171
## [907] -0.30481062 -0.30481062 0.82682868 -0.30481062 0.82682868 -0.99975517
## [913] -0.89792768 0.51397846 0.77389068 0.85090352 -0.99975517 -0.38778164
## [919] 0.77389068 0.77389068 0.77389068 0.77389068 -0.83177474 -0.83177474
## [925] -0.94251445 -0.94251445 0.81674261 0.18478174 0.95105465 0.96836446
## [931] 0.37960774 0.63673801 0.05305349 0.37960774 0.37960774 0.37960774
## [937] 0.37960774 -0.99388865 0.89399666 -0.85551998 -0.67677196 -0.96611777
## [943] 0.39592515 0.03539830 0.90178835 0.08836869 -0.26237485 0.77389068
## [949] -0.50636564 -0.26237485 0.82682868 -0.99388865 -0.26237485 0.82682868
## [955] 0.77389068 0.77389068 0.58061118 0.29636858 -0.89792768 -0.57338187
## [961] -0.13235175 -0.26237485 -0.30481062 0.74511316 0.77389068 0.74511316
## [967] -0.30481062 -0.91652155 0.25382336 -0.26237485 0.89399666 0.63673801
## [973] -0.11478481 -0.98803162 -0.99388865 -0.04424268 0.74511316 0.77389068
## [979] -0.04424268 0.98662759 0.25382336 0.77389068 -0.15862267 -0.96611777
## [985] 0.74511316 -0.38778164 -0.38778164 -0.26237485 -0.50636564 -0.26237485
## [991] -0.99388865 0.74511316 -0.30481062 -0.91652155 0.43616476 0.43616476
## [997] 0.85090352 0.82682868 0.68326171 -0.94828214 0.77389068 -0.30481062
## [1003] -0.73918070 -0.99388865 0.99287265 0.85090352 0.82682868 0.82682868
## [1009] -0.76825466 -0.98803162 0.77389068 -0.50636564 -0.38778164 -0.38778164
## [1015] -0.30481062 0.77389068 0.99287265 0.99287265 0.25382336 0.49871315
## [1021] 0.89399666 0.89399666 0.89399666 0.89399666 0.77389068 0.27090579
## [1027] -0.89792768 0.03539830 -0.77946607 -0.77946607 -0.77946607 -0.77946607
## [1033] 0.98023966 -0.50639163 -0.30481062 -0.50636564 -0.50636564 -0.97053528
## [1039] -0.99388865 -0.87329730 -0.50636564 -0.50636564 -0.50636564 -0.50636564
## [1045] -0.50636564cos(head(d2$HP))## [1] 0.5253220 -0.9524130 -0.1103872 -0.1103872 0.2666429 0.1191801factorial(head(d2$HP))## [1] 1.196222e+56 8.320987e+81 7.156946e+118 7.156946e+118 2.039788e+46
## [6] 2.350561e+78abs(head(d2$HP))## [1] 45 60 80 80 39 58sqrt(head(d2$HP))## [1] 6.708204 7.745967 8.944272 8.944272 6.244998 7.615773tan(head(d2$HP))## [1] 1.6197752 0.3200404 9.0036549 9.0036549 3.6145544 8.3308569asin(head(d2$HP))## Warning in asin(head(d2$HP)): NaNs produced## [1] NaN NaN NaN NaN NaN NaNViewing Previously Loaded Named-Objects.
ls()## [1] "d.mat" "d2" "dens" "md"ls(pattern = 'v')## character(0)ls(pattern = 'da')## character(0)var(head(d2$HP))## [1] 293.8667median(d2$HP)## [1] 68mode(d2$HP)## [1] "numeric"Selecting and Displaying Parts of a Object.
head(d2[1])head(d2[1:4])head(d2[-1])head(d2[d2>7])## [1] "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur"
## [5] "Charmander" "Charmeleon"head(d2[d2<5 | d2>10],n=10)## [1] "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur"
## [5] "Charmander" "Charmeleon" "Charizard" "Mega Charizard"
## [9] "Mega Charizard X" "Squirtle"head(d2[d2<5 | d2>10])## [1] "Bulbasaur" "Ivysaur" "Venusaur" "Mega Venusaur"
## [5] "Charmander" "Charmeleon"d2[100,8]## [1] 90d2[150,1:6]Sorting and Rearranging a Object.
sort(head(d2$HP))## [1] 39 45 58 60 80 80sort(head(d2$HP), decreasing = TRUE)## [1] 80 80 60 58 45 39sort(head(d2$HP),na.last = NA)## [1] 39 45 58 60 80 80sort(head(d2$HP),na.last = TRUE)## [1] 39 45 58 60 80 80sort(head(d2$HP),na.last = FALSE)## [1] 39 45 58 60 80 80order(d2$HP)## [1] 352 64 65 167 218 263 417 427 526 82 109 900 961 339
## [15] 1026 25 26 121 123 130 152 156 180 238 359 370 494 501
## [29] 642 974 1010 350 31 33 60 66 67 113 127 178 207 221
## [43] 234 275 289 318 440 707 857 687 485 49 50 320 321 340
## [57] 666 667 773 778 884 958 5 201 17 21 29 55 68 69
## [71] 75 79 83 92 94 95 132 143 158 211 213 223 269 306
## [85] 329 332 335 337 343 373 411 428 449 480 490 496 535 540
## [99] 643 645 675 704 766 836 871 886 888 964 966 977 985 992
## [113] 191 607 769 983 804 894 968 994 409 444 824 923 924 10
## [127] 424 474 702 783 810 827 1 14 18 34 57 124 154 198
## [141] 277 280 291 292 293 310 324 383 395 400 415 445 487 506
## [155] 520 547 568 593 602 604 620 639 647 679 682 725 730 775
## [169] 779 794 820 851 873 874 878 879 916 997 1006 44 715 945
## [183] 862 865 171 249 542 860 890 1009 518 545 826 15 37 38
## [197] 70 73 89 100 101 110 122 137 140 141 159 204 219 252
## [211] 271 290 299 307 314 325 327 358 360 362 363 364 365 366
## [225] 380 396 398 407 434 467 468 469 470 525 529 570 571 572
## [239] 573 574 575 609 611 613 617 634 651 661 663 673 693 700
## [253] 712 721 806 808 819 843 844 854 880 881 947 950 953 962
## [267] 970 988 990 688 897 111 112 739 895 980 477 802 943 669
## [281] 770 784 828 841 27 41 77 84 85 96 97 131 153 172
## [295] 212 222 225 235 237 241 266 288 441 442 471 481 504 513
## [309] 599 622 640 671 677 692 697 710 718 742 831 834 858 902
## [323] 904 912 917 763 523 823 863 995 996 6 202 530 668 738
## [337] 812 1005 1017 1018 11 483 728 767 795 829 932 972 2 16
## [351] 32 35 36 58 61 62 114 125 126 128 133 134 138 139
## [365] 157 181 199 209 245 248 258 267 270 274 311 326 328 330
## [379] 336 338 345 346 347 367 371 372 376 377 388 392 394 412
## [393] 423 450 488 491 495 497 498 499 503 516 548 567 594 603
## [407] 627 644 646 652 676 680 690 705 706 713 714 796 797 855
## [421] 872 889 907 908 910 963 967 993 1002 1015 1035 45 192 351
## [435] 764 942 984 618 685 776 781 791 800 811 1003 22 410 521
## [449] 353 425 426 475 478 608 19 20 30 71 72 76 80 90
## [463] 102 103 115 144 145 149 161 162 163 164 165 173 175 176
## [477] 177 205 224 240 243 255 260 273 279 378 379 431 432 446
## [491] 514 515 560 561 596 605 625 664 683 708 726 731 807 830
## [505] 852 909 911 951 954 998 1007 1008 413 716 786 492 524 626
## [519] 788 818 940 341 342 531 538 565 566 821 848 885 891 913
## [533] 959 1027 546 695 973 42 47 63 86 160 179 214 226 230
## [547] 232 251 261 262 300 308 309 315 319 333 344 361 368 369
## [561] 374 375 381 384 385 389 390 391 399 419 420 421 422 437
## [575] 500 502 507 536 537 541 551 552 569 588 623 648 649 650
## [589] 654 655 662 681 694 701 733 735 882 887 892 915 919 920
## [603] 921 922 948 955 956 965 978 982 1001 1011 1016 1025 519 689
## [617] 762 809 929 356 665 740 771 772 805 896 969 981 1019 51
## [631] 52 287 403 404 941 544 670 703 792 793 28 39 40 56
## [645] 59 166 216 229 236 239 253 256 257 276 281 282 283 401
## [659] 402 416 430 472 510 556 557 562 576 578 595 600 610 612
## [673] 614 621 631 635 638 641 660 672 678 686 711 768 790 832
## [687] 867 868 869 870 875 877 901 918 986 987 1013 1014 476 508
## [701] 528 615 717 486 727 814 864 7 8 9 203 322 323 777
## [715] 785 798 817 849 850 914 12 13 484 747 748 749 750 3
## [729] 4 74 87 91 93 98 99 117 118 146 155 182 183 200
## [743] 264 265 312 313 331 348 393 397 435 436 451 452 453 454
## [757] 455 456 457 458 459 489 577 586 619 674 691 720 722 780
## [771] 845 846 847 856 859 938 952 975 991 1004 1039 46 801 813
## [785] 23 24 543 930 354 479 129 206 208 215 278 286 482 505
## [799] 558 564 606 624 632 709 737 743 774 825 833 837 876 903
## [813] 414 559 782 803 765 861 944 1028 729 753 754 43 78 81
## [827] 88 104 105 116 142 185 186 187 188 189 190 220 227 228
## [841] 233 259 268 284 285 296 334 405 406 438 511 549 550 580
## [855] 597 822 898 939 971 1021 1022 1023 1024 193 581 744 745 746
## [869] 758 759 741 1029 1030 1031 1032 1000 48 106 107 108 135 136
## [883] 168 169 242 244 246 247 294 418 433 447 448 473 601 653
## [897] 719 732 789 816 835 853 905 906 999 493 866 931 934 935
## [911] 936 937 960 429 197 210 231 254 272 298 301 302 305 316
## [925] 317 382 443 460 461 462 463 466 517 527 555 579 587 589
## [939] 590 592 698 734 751 752 760 761 883 899 949 989 1012 1036
## [953] 1037 1041 1042 1043 1044 1045 799 522 629 630 355 119 120 147
## [967] 150 151 464 465 633 636 656 657 658 659 1038 194 195 196
## [981] 303 304 928 532 533 539 840 723 724 927 408 439 553 563
## [995] 582 598 628 684 736 976 979 509 696 53 297 554 616 585
## [1009] 591 637 893 957 1020 787 815 217 755 756 757 838 839 170
## [1023] 174 386 534 946 925 926 54 1033 357 349 512 583 584 184
## [1037] 699 387 250 1040 842 933 148 295 1034rank(d2)## Name Total HP Attack Defence Sp_attack Sp_defence
## 7398.0 7687.0 8298.0 7927.0 7423.0 7424.0 7421.0
## Speed <NA> <NA> <NA> <NA> <NA> <NA>
## 7814.0 7815.0 8202.0 8317.0 7379.0 7806.0 7415.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7949.0 7402.0 8320.0 7699.0 7367.0 7805.0 8036.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8035.0 8034.0 7889.0 8081.0 7899.0 8080.0 7898.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8193.0 7562.0 7540.0 7336.0 8039.0 7890.0 8075.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7896.0 8116.0 7909.0 8117.0 7910.0 7988.0 7989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7986.0 7987.0 7990.0 7985.0 7443.0 7442.0 8313.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7928.0 7993.0 7884.0 7690.0 8327.0 8358.0 7613.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8003.0 7611.0 8305.0 8020.0 8021.0 8297.0 8296.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7499.0 7825.0 7526.0 7826.0 7945.0 7871.0 7872.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8027.0 7888.0 8063.0 7615.0 7779.0 8060.0 7639.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7337.0 8049.0 8050.0 8051.0 7317.0 7698.0 7324.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7798.0 7763.0 7762.0 7761.0 7371.0 8321.0 8302.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8242.0 8243.0 7600.0 7836.0 7630.0 7843.0 7616.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7839.0 8053.0 7892.0 8079.0 7897.0 8177.0 7918.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8175.0 7915.0 7916.0 7770.0 7771.0 7561.0 7831.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7502.0 7501.0 8133.0 7492.0 7633.0 7845.0 7976.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7880.0 8144.0 7446.0 7596.0 7653.0 7599.0 7835.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8006.0 7522.0 7678.0 7715.0 7706.0 8311.0 7545.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7557.0 7558.0 7830.0 7475.0 7786.0 7866.0 7665.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7664.0 7737.0 7712.0 8322.0 7929.0 8094.0 8093.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7420.0 8234.0 7700.0 7852.0 7673.0 8129.0 7614.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8130.0 8209.0 8208.0 7971.0 7879.0 8128.0 7695.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7542.0 7768.0 8044.0 7891.0 8240.0 7767.0 7646.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7847.0 7727.0 7500.0 7538.0 7827.0 8294.0 7692.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7572.0 8056.0 8004.0 8005.0 7696.0 7697.0 7321.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7796.0 8185.0 7348.0 7801.0 8350.0 7936.0 7965.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7877.0 7515.0 7510.0 7511.0 7951.0 7874.0 7875.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7950.0 7431.0 7364.0 7940.0 7479.0 8071.0 8280.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8261.0 7471.0 7565.0 8135.0 7589.0 7671.0 7995.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7735.0 7734.0 8197.0 7343.0 7470.0 7434.0 7726.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8033.0 7444.0 7679.0 8254.0 8255.0 7982.0 8338.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7784.0 7569.0 7331.0 7800.0 7370.0 7785.0 7354.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8217.0 8048.0 7672.0 8166.0 7694.0 7323.0 8220.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8219.0 8343.0 8334.0 8070.0 7553.0 8285.0 7979.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8176.0 7917.0 7963.0 8287.0 8331.0 7603.0 8043.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7581.0 7527.0 7609.0 8210.0 7919.0 8188.0 7628.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8073.0 8123.0 7912.0 8152.0 7660.0 7848.0 8182.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8241.0 8288.0 8178.0 7765.0 8228.0 8041.0 7456.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7816.0 8090.0 8002.0 7489.0 7780.0 8164.0 7675.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7674.0 7850.0 7705.0 8029.0 7503.0 8058.0 8204.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8180.0 8283.0 7666.0 8181.0 7546.0 7764.0 7957.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7382.0 8076.0 7551.0 8218.0 7729.0 8065.0 8281.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7925.0 7752.0 7667.0 7416.0 8269.0 7638.0 8122.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7911.0 8256.0 7451.0 7380.0 7807.0 7974.0 7788.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8225.0 7921.0 8054.0 7954.0 8355.0 7937.0 7742.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7860.0 8336.0 8155.0 7366.0 7413.0 7534.0 7748.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7746.0 7751.0 8132.0 8000.0 8148.0 8232.0 8227.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8329.0 8025.0 8077.0 7707.0 7595.0 7834.0 8221.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7789.0 8151.0 7391.0 8173.0 8303.0 8172.0 7992.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7994.0 8142.0 8326.0 7749.0 7559.0 7773.0 7649.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7355.0 7998.0 8167.0 7488.0 8109.0 7907.0 7790.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7867.0 7346.0 7723.0 7322.0 7797.0 7792.0 7791.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7868.0 7544.0 7778.0 7865.0 8046.0 7962.0 8308.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7680.0 8103.0 7642.0 8224.0 7412.0 8140.0 7913.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8314.0 8315.0 7999.0 7407.0 7810.0 8257.0 8200.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7641.0 8198.0 8268.0 8300.0 7578.0 7404.0 7405.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8222.0 7327.0 7799.0 8349.0 8138.0 7755.0 8192.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7360.0 8325.0 7455.0 7467.0 7357.0 7441.0 7739.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7464.0 7332.0 7344.0 7563.0 7956.0 7414.0 7811.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7812.5 7812.5 7703.0 8153.0 7358.0 7803.0 7533.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7531.0 8271.0 7433.0 7318.0 7794.0 8337.0 8186.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7606.0 7838.0 8196.0 8131.0 8316.0 7438.0 7676.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7622.0 8089.0 7757.0 7356.0 8143.0 8110.0 7908.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7369.0 7948.0 7947.0 7873.0 8087.0 8083.0 8088.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7730.0 7858.0 7731.0 7859.0 7721.0 7854.0 7637.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7846.0 8082.0 7900.0 7691.0 7491.0 7821.0 7822.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7822.5 8276.0 7636.0 8260.0 7432.0 7966.0 7684.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8045.0 8061.0 7550.0 8207.0 8206.0 8205.0 7375.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7374.0 7716.0 7717.0 8150.0 7758.0 7759.0 7396.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8104.0 7466.0 8078.0 8147.0 7363.0 7401.0 8335.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7931.0 7932.0 7970.0 7450.0 8299.0 8011.0 7397.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7575.0 7427.0 7426.0 8145.0 7597.0 7329.0 7519.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7518.0 7399.0 7747.0 7861.0 7964.0 7668.0 7607.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8067.0 7435.0 8216.0 8170.0 7394.0 7393.0 7386.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7958.0 7648.0 7425.0 8199.0 7601.0 7590.0 7594.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7833.0 7977.0 8098.0 7750.0 7862.0 7662.0 7663.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8168.0 7514.0 7469.0 8265.0 7410.0 7568.0 7753.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7781.0 8187.0 7316.0 7793.0 8319.0 7772.0 7736.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8095.0 8235.0 7543.0 7769.0 8253.0 8344.0 7732.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7605.0 7610.0 7775.0 8057.0 7591.0 7832.0 8062.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7532.0 7586.0 8105.0 7902.0 7904.5 7904.5 7904.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7904.5 8290.0 7946.0 7353.0 7496.0 8012.0 7657.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8086.0 7604.0 7837.0 7468.0 8032.0 7776.0 7480.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8141.0 7914.0 7338.0 8301.0 8183.0 8137.0 8136.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8244.0 8038.0 7548.0 8010.0 7493.0 8113.0 8023.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8318.0 7741.0 7661.0 8213.0 8066.0 7738.0 8018.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8159.0 8019.0 8160.0 8017.0 8158.0 7978.0 7980.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8037.0 8267.0 8286.0 7383.0 8352.0 8100.0 7384.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7602.0 8332.0 8230.0 7520.0 7556.0 7349.0 7802.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8250.0 7644.0 7453.0 8277.0 8014.0 8134.0 8247.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8119.0 8139.0 8223.0 7733.0 8295.0 8324.0 8124.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7461.0 8323.0 8028.0 7740.0 7362.0 7804.0 8115.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7718.0 7719.0 7483.0 7820.0 7481.0 7817.0 7818.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7818.5 7782.0 7535.0 7472.0 8127.0 8126.0 8154.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8341.0 7933.0 7449.0 8251.0 7411.0 7339.0 7340.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8272.0 7593.0 8357.0 8356.0 7960.0 7436.0 7624.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7626.0 7625.0 8191.0 7528.0 8092.0 7525.0 8226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8292.0 8291.0 8293.0 7486.0 8120.0 7549.0 7701.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7552.0 7580.0 7330.0 7583.0 7689.0 7326.0 7693.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7592.0 7566.0 7567.0 7710.0 7708.0 7711.0 8278.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7536.0 7537.0 7547.0 7368.0 7745.0 7724.0 7419.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7352.0 7582.0 7655.0 7474.0 7365.0 7473.0 8146.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7319.0 8215.0 7920.0 7952.0 7953.0 7523.0 7617.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7619.0 8024.0 7377.0 7387.0 8107.0 7390.0 8312.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7777.0 7656.0 7530.0 7487.0 8359.0 7677.0 7728.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8310.0 7448.0 8245.0 8306.0 8258.0 7923.0 8248.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7922.0 8091.0 8353.0 7725.0 7857.0 7722.0 7855.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7856.0 7704.0 7853.0 7942.0 7869.0 7598.0 7429.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8072.0 7428.0 7564.0 7389.0 7490.0 7584.0 7585.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7632.0 7844.0 7400.0 7498.0 7574.0 7573.0 8233.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8121.0 8195.0 8307.0 7743.0 8068.0 7570.0 7576.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7577.0 8165.0 7612.0 8015.0 8016.0 7588.0 7554.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7944.0 7870.0 7669.0 7505.0 7320.0 7795.0 8201.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7345.0 8229.0 8179.0 7685.0 7774.0 7376.0 7359.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8169.0 7508.0 7439.0 7440.0 7659.0 7658.0 8284.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8282.0 7328.0 7350.0 8231.0 7654.0 7485.0 7408.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7621.0 8174.0 7620.0 7709.0 8030.0 8270.0 8064.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7893.0 7894.5 7894.5 7627.0 7840.0 7841.5 7841.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7372.0 7351.0 7996.0 7997.0 8339.0 8346.0 8360.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7938.0 7939.0 7497.0 7824.0 7670.0 7849.0 8309.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8106.0 7482.0 7484.0 7744.0 8259.0 7682.0 8055.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7392.0 8059.0 8040.0 8273.0 8262.0 8345.0 7643.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7640.0 7422.0 8304.0 7463.0 7462.0 8009.0 7885.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7886.5 7886.5 7478.0 8096.0 8099.0 7901.0 7760.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7863.0 7864.0 8330.0 7930.0 7783.0 8263.0 7973.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7975.0 7494.0 7335.0 7579.0 7756.0 7967.0 8149.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8111.0 8112.0 8214.0 7373.0 7388.0 8211.0 8274.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7452.0 8007.0 8022.0 8328.0 7618.0 8118.0 8013.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8069.0 8279.0 8157.0 7961.0 7876.0 7713.0 8275.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8252.0 7959.0 7395.0 7513.0 7495.0 7688.0 7647.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7714.0 8238.0 8239.0 8236.0 8237.0 7460.0 7459.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8190.0 7754.0 7991.0 7403.0 8031.0 8340.0 7417.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7702.0 7645.0 7983.0 7881.0 7882.5 7882.5 7766.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7787.0 8047.0 7981.0 8203.0 7378.0 8354.0 7943.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7941.0 7635.0 8249.0 8097.0 8125.0 8074.0 7437.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8189.0 7521.0 7686.0 8171.0 7631.0 8102.0 7458.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7457.0 7381.0 7504.0 8008.0 7984.0 8246.0 7623.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7541.0 8333.0 7524.0 7430.0 7516.0 8342.0 7385.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8101.0 7409.0 7447.0 7334.0 7571.0 7333.0 8156.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8114.0 7465.0 7347.0 7361.0 8264.0 8266.0 7924.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8163.0 7418.0 7445.0 7629.0 8161.0 8052.0 7650.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7652.0 7651.0 7681.0 7968.0 7634.0 8001.0 8026.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7477.0 8162.0 7972.0 8108.0 7955.0 7325.0 7560.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8042.0 8184.0 7587.0 8212.0 7539.0 7828.0 7683.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7851.0 7969.0 7878.0 7476.0 7454.0 7507.0 7342.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7506.0 7341.0 7529.0 7517.0 7512.0 7509.0 8347.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7934.0 8348.0 7935.0 7555.0 7829.0 7720.0 8289.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7926.0 8351.0 8085.0 8084.0 7608.0 8194.0 7406.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7808.0 7809.0 595.0 1273.5 2525.0 3535.5 555.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1273.5 2590.0 3580.5 3580.5 582.5 1273.5 2577.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3577.5 54.0 121.0 973.5 54.0 121.0 973.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2007.0 228.0 700.0 1841.5 2967.0 230.0 230.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1324.5 1324.5 238.5 1472.0 297.5 1475.5 616.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1419.5 1930.5 1930.5 512.5 512.5 1719.5 1719.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 265.5 888.0 2420.5 262.0 888.0 2420.5 623.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1916.0 331.5 331.5 2420.5 2420.5 258.0 1427.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 162.5 1735.5 616.0 973.5 1978.0 293.5 1273.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 539.0 1719.5 244.5 244.5 1384.5 1384.5 315.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 315.5 315.5 1466.0 1466.0 616.0 2395.0 539.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1735.5 851.0 2900.5 512.5 950.5 2450.5 572.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1256.0 2395.0 3446.5 539.0 1273.5 2420.5 512.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 966.5 1978.0 667.5 2464.0 512.5 512.5 966.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 966.5 2007.0 2007.0 1313.5 1313.5 2395.0 2395.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 587.5 587.5 1978.0 3063.0 1978.0 629.5 1780.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 914.5 914.5 572.5 1809.0 629.5 1829.0 629.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 629.5 2395.0 2395.0 539.0 2525.0 572.5 1273.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2395.0 3446.5 950.5 635.5 1916.0 629.5 1829.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 653.5 1978.0 629.5 2577.0 2577.0 616.0 1384.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1384.5 1735.5 1735.5 950.5 687.0 1978.0 1978.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 695.0 1930.5 1719.5 1427.5 1978.0 3063.0 327.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1466.0 616.0 1719.5 687.0 2508.0 1765.0 1765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2395.0 1735.5 1978.0 2007.0 2395.0 3446.5 1978.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 116.5 2627.0 3608.5 2597.5 297.5 629.5 1427.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2525.0 2525.0 2525.0 973.5 859.0 2007.0 859.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2007.0 2464.0 3498.0 2627.0 3025.5 3025.5 3025.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3025.5 3025.5 3025.5 512.5 1366.0 3446.5 4023.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4839.0 4839.0 3446.5 595.0 1273.5 2525.0 555.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1273.5 2590.0 582.5 1273.5 2577.0 129.0 1327.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 238.5 1725.0 244.5 966.5 223.0 1256.0 2597.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 653.5 1765.0 121.0 130.0 125.5 162.5 1273.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 616.0 1809.0 284.5 888.0 2450.5 3496.5 1978.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 223.0 1366.0 1313.5 2395.0 223.0 687.0 1765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 879.5 48.5 1384.5 966.5 125.5 1419.5 2525.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2525.0 1273.5 1978.0 1978.0 1427.5 673.0 1273.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1735.5 315.5 1780.0 1327.5 1419.5 2450.5 3496.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 512.5 1719.5 1466.0 2395.0 3446.5 2420.5 2395.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3446.5 1419.5 653.5 2395.0 223.0 1419.5 223.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1719.5 1313.5 1313.5 512.5 1900.5 653.5 1930.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1780.0 653.5 2395.0 3446.5 2627.0 653.5 2395.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2464.0 1780.0 223.0 125.5 1735.5 539.0 879.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 888.0 1978.0 2627.0 3025.5 3025.5 3025.5 512.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1313.5 3446.5 4400.0 4023.0 4023.0 3446.5 572.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1273.5 2577.0 3577.5 572.5 1273.5 2577.0 3577.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 572.5 1273.5 2597.5 3582.0 134.5 1366.0 155.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 155.5 1366.0 1366.0 54.0 121.0 973.5 121.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 950.5 134.5 687.0 1900.5 134.5 687.0 1900.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 258.0 1735.5 258.0 1466.0 56.0 269.5 2470.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3499.5 249.5 1728.0 327.5 1765.0 284.5 1466.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3980.5 248.0 1742.0 147.0 155.5 879.5 1978.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 148.5 1821.5 51.0 913.0 235.5 1256.0 943.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1900.5 943.0 1900.5 653.5 1419.5 2577.0 3577.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 284.5 1313.5 2450.5 327.5 1829.0 2966.0 1273.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1273.5 1419.5 1419.5 1256.0 525.0 1786.0 539.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1765.0 2925.5 1256.0 2395.0 539.0 1765.0 2925.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1809.0 653.5 1809.0 879.5 315.5 687.0 2508.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 667.5 1829.0 572.5 1978.0 3063.0 1743.5 1743.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1765.0 1765.0 297.5 1787.5 551.0 1787.5 512.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2395.0 859.0 2007.0 859.0 2007.0 116.5 2627.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1366.0 1366.0 1366.0 1366.0 1466.0 327.5 1735.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2900.5 327.5 1735.5 1765.0 1735.5 1780.0 2927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 235.5 512.5 1900.5 3025.5 315.5 1313.5 2577.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 695.0 1930.5 1930.5 1930.5 653.5 512.5 1366.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3446.5 4400.0 512.5 1366.0 3446.5 4400.0 3025.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3025.5 3025.5 3446.5 4400.0 3446.5 4400.0 3980.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4806.5 3980.5 4806.5 4023.0 4839.0 3446.5 3446.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3446.5 3446.5 3446.5 595.0 1273.5 2525.0 555.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1273.5 2590.0 582.5 1273.5 2577.0 162.5 687.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1930.5 223.0 1313.5 52.0 946.5 240.0 885.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2516.0 284.5 2464.0 851.0 2007.0 851.0 2007.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 137.0 1379.5 1379.5 1379.5 1379.5 159.0 1821.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1273.5 653.5 2007.0 265.5 1719.5 629.5 1829.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1912.5 698.0 2019.5 851.0 1900.5 3025.5 2007.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2420.5 572.5 1725.0 293.5 637.5 1841.5 512.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2395.0 315.5 572.5 134.5 1321.5 1930.5 512.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1313.5 3446.5 4400.0 966.5 293.5 2525.0 3535.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 653.5 2525.0 653.5 2395.0 512.5 1978.0 1728.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 653.5 1765.0 695.0 662.0 1994.5 3065.0 2450.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2597.5 2464.0 2597.5 2597.5 2627.0 2627.0 2633.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2464.0 2525.0 2525.0 2450.5 2577.0 2597.5 2470.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3499.5 2525.0 2525.0 1900.5 1466.0 2508.0 2508.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2508.0 2508.0 2508.0 3025.5 3025.5 3025.5 4023.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4023.0 3446.5 3980.5 4023.0 4023.0 3446.5 1900.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3446.5 3446.5 3446.5 3446.5 4472.0 3446.5 551.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1324.5 2535.0 551.0 1329.5 2535.0 551.0 1324.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2535.0 232.0 1366.0 265.5 908.5 2395.0 290.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1474.0 592.0 2019.5 592.0 2019.5 592.0 2019.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 321.5 1943.5 241.0 863.0 1946.5 327.5 2017.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 284.5 966.5 2464.0 623.5 1384.5 635.5 2430.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1473.0 2633.5 539.0 1273.5 2420.5 323.5 946.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2432.0 1780.0 1780.0 572.5 943.0 2395.0 235.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 879.5 1930.5 284.5 1900.5 284.5 1900.5 1765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1765.0 321.5 855.0 2472.0 587.5 587.5 1900.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2627.0 1900.5 2627.0 1773.0 629.5 1930.5 698.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1946.5 1978.0 527.5 527.5 1916.0 859.0 2007.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1260.0 2928.0 637.5 1821.5 653.5 2450.5 512.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1809.0 315.5 966.5 1978.0 315.5 908.5 1978.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 539.0 1819.0 539.0 973.5 2597.5 667.5 1829.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1388.0 587.5 2007.0 323.5 1775.5 667.5 1900.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1809.0 597.0 1817.5 539.0 1948.0 512.5 1466.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2508.0 265.5 1273.5 2464.0 667.5 1930.5 265.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 908.5 2508.0 616.0 1313.5 2627.0 539.0 2420.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2464.0 539.0 2007.0 1815.5 1815.5 851.0 2450.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1930.5 527.5 1916.0 687.0 1978.0 1978.0 851.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2450.5 908.5 2450.5 1919.5 1919.5 512.5 1366.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3446.5 879.5 2897.0 3025.5 3025.5 3025.5 3025.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3025.5 3025.5 3025.5 4023.0 4023.0 3446.5 3446.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3947.0 4400.0 4400.0 3025.5 3025.5 3446.5 3446.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3446.5 580.0 1273.5 2577.0 548.0 1287.0 2590.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 582.5 1273.5 2577.0 3608.5 148.5 1377.0 269.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 945.0 2022.0 116.5 128.0 1321.5 891.0 2428.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 527.5 912.0 2899.0 851.0 2587.0 698.0 2007.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1817.5 859.0 1784.5 1784.5 629.5 1475.5 2395.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2395.0 692.5 1774.0 692.5 1900.5 297.5 1912.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 547.0 2395.0 616.0 1994.5 653.5 2395.0 300.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1911.0 883.5 2514.5 883.5 2514.5 2525.0 2395.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1424.0 2395.0 512.5 1725.0 3446.5 1809.0 555.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1821.5 667.5 667.5 667.5 667.5 1994.5 1994.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1994.5 1994.5 530.5 2459.0 162.5 2597.5 4023.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4023.0 3446.5 1941.0 4405.0 3446.5 4400.0 3446.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4023.0 3446.5 616.0 1366.0 2577.0 616.0 1366.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2577.0 616.0 1366.0 2577.0 244.5 859.0 1930.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 230.0 1329.5 512.5 1256.0 2395.0 674.0 1840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1837.0 1837.0 1837.0 1837.0 530.5 1775.5 284.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 284.5 1943.5 1943.5 1943.5 47.0 3534.0 539.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2007.0 950.5 2395.0 249.5 1728.0 223.0 1900.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 293.5 1273.5 616.0 1900.5 687.0 2395.0 125.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 315.5 2450.5 1930.5 1978.0 1978.0 146.0 2577.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 616.0 1900.5 1313.5 2590.0 2958.5 1466.0 2395.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1900.5 1930.5 1427.5 1837.0 1829.0 1930.5 2469.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 512.5 1366.0 3446.5 2958.5 2958.5 2958.5 2958.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 116.5 1256.0 4023.0 4023.0 2958.5 2958.5 2958.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2958.5 2958.5 2958.5 2958.5 3446.5 4023.0 4023.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4757.0 3446.5 3446.5 1366.0 2627.0 2958.5 2958.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3446.5 512.5 3446.5 572.5 1366.0 2577.0 572.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1366.0 2577.0 572.5 1366.0 2577.0 265.5 1765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 162.5 888.0 2007.0 48.5 667.5 2420.5 162.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1735.5 223.0 1765.0 258.0 1978.0 291.0 1930.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 258.0 1978.0 155.5 1313.5 2450.5 235.5 1930.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1930.5 587.5 2450.5 1829.0 284.5 1978.0 158.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2412.5 2412.5 539.0 2525.0 572.5 1900.5 551.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2430.5 244.5 908.5 2450.5 244.5 908.5 2450.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2508.0 1466.0 2450.5 2428.5 2508.0 1916.0 258.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2007.0 1809.0 1427.5 50.0 1829.0 1809.0 1809.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1809.0 1829.0 1829.0 1431.5 1431.5 653.5 2395.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2420.5 2420.5 2420.5 2420.5 2597.5 258.0 1313.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3446.5 4472.0 3980.5 4472.0 3980.5 4056.0 6687.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 950.5 2897.0 2897.0 3446.5 3025.5 3025.5 3025.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3025.5 2395.0 4023.0 4023.0 1596.0 3244.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 958.0 2989.0 4822.5 4822.5 4822.5 1446.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3051.0 4852.0 4852.0 1596.0 2200.5 3244.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3766.5 3766.5 1114.5 3556.0 5213.5 5213.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 416.5 2765.0 4638.0 1114.5 3766.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 774.0 1596.0 3244.5 3244.5 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 4638.0 2765.0 4226.0 5634.0 1751.0 3482.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5170.0 4226.0 5927.0 928.5 928.5 4487.5 4487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 7147.0 1114.5 4638.0 1596.0 3244.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 3244.5 3244.5 4226.0 15.5 15.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 1114.5 1114.5 2200.5 3766.5 3766.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 1114.5 3766.5 2765.0 5634.0 1114.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 192.0 1114.5 2765.0 2765.0 4226.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 2200.5 3766.5 5011.5 1114.5 5011.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2765.0 2765.0 5011.5 5011.5 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3766.5 5634.0 5634.0 5927.0 5927.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 2200.5 2487.5 2487.5 774.0 3244.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 5011.5 5011.5 6448.5 6448.5 416.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 1596.0 3244.5 3244.5 774.0 3244.5 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 2765.0 1114.5 3244.5 3244.5 5927.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3244.5 3244.5 2200.5 2200.5 5634.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3766.5 5011.5 6448.5 7312.0 3766.5 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 416.5 2765.0 1596.0 5011.5 416.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2200.5 4226.0 3766.5 3766.5 3766.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 4638.0 85.5 5927.0 5927.0 7031.5 1866.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3766.5 7031.5 3766.5 3766.5 3766.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 416.5 3244.5 5011.5 5011.5 7254.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 5634.0 5634.0 5634.0 5634.0 1297.0 3482.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5770.0 6509.0 6509.0 6509.0 6223.5 1596.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 958.0 2989.0 4822.5 2200.5 3766.5 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 5343.0 3244.5 6223.5 1114.5 2765.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5343.0 4638.0 6940.5 85.5 2200.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 2765.0 1114.5 3766.5 2765.0 4226.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 4638.0 4226.0 6223.5 4226.0 5634.0 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 4638.0 2765.0 416.5 4638.0 3766.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 3766.5 5927.0 3244.5 5927.0 5927.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1866.5 7296.5 4226.0 2200.5 4638.0 6223.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 4638.0 3244.5 5634.0 3766.5 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 85.5 5011.5 5011.5 2765.0 3244.5 5634.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 2200.5 6223.5 3766.5 3244.5 774.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 5343.0 3766.5 1596.0 4638.0 4638.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 5634.0 5343.0 4487.5 2765.0 774.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 1596.0 1596.0 5927.0 7314.5 5634.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 2200.5 4226.0 6223.5 6223.5 6509.0 6509.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 1114.5 2200.5 4226.0 4226.0 1596.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5011.5 2200.5 4226.0 6223.5 6223.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 928.5 928.5 4822.5 4822.5 1596.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 2200.5 3244.5 1114.5 3244.5 5011.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5634.0 1114.5 3244.5 1114.5 3244.5 275.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 928.5 3998.5 3998.5 1114.5 4226.0 3244.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5011.5 7212.0 561.5 3482.5 1.0 3595.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5233.5 6389.5 4452.0 7167.0 2200.5 416.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 2200.5 2200.5 2200.5 2200.5 2200.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 416.5 3244.5 3244.5 1114.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 3244.5 3244.5 3766.5 3766.5 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 1596.0 4226.0 4226.0 7031.5 7277.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 4226.0 3244.5 5011.5 3244.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5011.5 2200.5 4226.0 1596.0 4638.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4487.5 4487.5 5634.0 5634.0 2200.5 6618.5 1402.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3556.0 1114.5 3244.5 3935.0 5458.0 1596.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 85.5 5927.0 4226.0 4226.0 4226.0 4226.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1446.5 3595.0 3595.0 85.5 1114.5 6092.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3766.5 5927.0 2200.5 5011.5 5011.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6618.5 774.0 2765.0 2765.0 6223.5 1402.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3766.5 5927.0 5927.0 1114.5 3244.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5011.5 5011.5 5011.5 5011.5 5011.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 6223.5 6223.5 6223.5 6223.5 6448.5 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 2200.5 2200.5 2200.5 2200.5 2765.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 1446.5 3595.0 4767.5 2552.0 3595.0 5233.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2765.0 5343.0 3051.0 4852.0 898.5 4791.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3244.5 5011.5 1114.5 3244.5 3962.5 6051.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 3244.5 1114.5 3244.5 3244.5 3244.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 4226.0 3244.5 2765.0 5343.0 1596.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4767.5 6673.5 4638.0 5634.0 7212.0 2765.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3244.5 6223.5 1956.5 4419.5 1596.0 3556.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6379.0 2940.0 3962.5 2200.5 85.5 6223.5 4767.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 2989.0 3998.5 6543.0 6543.0 7104.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 3998.5 6543.0 1114.5 4226.0 1866.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5213.5 4510.5 1956.5 4044.0 1596.0 3244.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 4226.0 4226.0 6618.5 6738.5 6223.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 5343.0 5458.0 3766.5 3766.5 4638.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 3998.5 3998.5 3244.5 1596.0 4226.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 2200.5 2200.5 2200.5 2200.5 4638.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6223.5 5634.0 5770.0 6618.5 7212.0 7212.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 5011.5 6223.5 4226.0 6223.5 6223.5 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 1596.0 3244.5 4638.0 3766.5 5634.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 4638.0 5927.0 1596.0 3244.5 1596.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1297.0 3595.0 2200.5 4638.0 2200.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 4638.0 4767.5 6781.0 2200.5 3517.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 4638.0 2765.0 4226.0 5343.0 3766.5 3962.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 6618.5 6379.0 6379.0 4638.0 5343.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 4638.0 6448.5 6840.0 4638.0 1596.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 416.5 1114.5 3244.5 1114.5 3244.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 4226.0 2200.5 3244.5 5927.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 6448.5 6448.5 6448.5 6448.5 4638.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 2200.5 3766.5 4452.0 928.5 928.5 2989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2612.0 4510.5 2765.0 4638.0 2200.5 5011.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 2765.0 4638.0 1596.0 3244.5 4226.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 6618.5 3517.0 4638.0 870.0 2437.5 4419.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5011.5 2765.0 2200.5 4226.0 4044.0 6697.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 6223.5 7266.5 2200.5 4226.0 1446.5 4510.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 3244.5 3244.5 774.0 3766.5 5343.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 2200.5 3244.5 3244.5 1751.0 3935.0 4767.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 5927.0 5011.5 2200.5 5011.5 6558.5 6558.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3766.5 4791.5 3051.0 5501.5 1596.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 4226.0 6223.5 4226.0 6618.5 5343.0 2989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2487.5 4452.0 5792.5 2765.0 5343.0 5770.0 5770.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5770.0 4852.0 4852.0 4852.0 4852.0 6223.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5501.5 5501.5 6940.5 6940.5 6940.5 5770.0 5770.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6223.5 4419.5 2913.0 3482.5 5488.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3051.0 4638.0 1297.0 2612.0 4452.0 4452.0 928.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1596.0 3517.0 4822.5 928.5 1596.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3517.0 5458.0 1446.5 2612.0 4822.5 3935.0 6904.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3962.5 5927.0 4638.0 3517.0 4510.5 4510.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3051.0 3244.5 3244.5 4822.5 6357.5 3517.0 5190.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2552.0 5458.0 1343.0 4452.0 2200.5 3766.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4419.5 1446.5 3517.0 2989.0 5190.5 4791.5 6904.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 4822.5 3962.5 2200.5 1596.0 3998.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2940.0 1402.0 5343.0 1956.5 1446.5 2612.0 3051.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 2765.0 4638.0 5343.0 2765.0 5927.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 6972.5 6972.5 6543.0 2612.0 7305.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5011.5 5011.5 5011.5 3998.5 4822.5 4822.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3766.5 5927.0 2200.5 3244.5 5011.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 5011.5 1866.5 5488.0 1796.0 2940.0 4791.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1796.0 6051.0 4638.0 4638.0 4638.0 4638.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1596.0 1596.0 4638.0 5343.0 4638.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 2200.5 2200.5 4226.0 6223.5 928.5 3998.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 4226.0 1114.5 3244.5 1866.5 3998.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 1343.0 2487.5 4452.0 2437.5 5634.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 4638.0 2765.0 5343.0 2765.0 5927.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 3244.5 3766.5 3244.5 3766.5 2765.0 3998.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4822.5 4226.0 1596.0 2765.0 4638.0 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 1402.0 1402.0 7124.5 7124.5 6558.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6525.0 4419.5 5213.5 6051.0 3051.0 7306.0 6051.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6051.0 6051.0 6051.0 5011.5 5634.0 3962.5 4487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3482.5 2552.0 5488.0 1751.0 7104.0 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 2200.5 3766.5 5011.5 2200.5 3766.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 6840.0 928.5 3998.5 6076.0 192.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1114.5 4226.0 1114.5 3244.5 1343.0 4452.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5634.0 3051.0 4044.0 416.5 5011.5 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 4226.0 6618.5 2487.5 4452.0 4226.0 1297.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3482.5 1114.5 4638.0 4638.0 2200.5 6223.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 1114.5 3244.5 1343.0 2940.0 2940.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5927.0 5809.5 4226.0 3244.5 3517.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2989.0 1596.0 3766.5 3766.5 1866.5 416.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 4638.0 4638.0 3244.5 4226.0 2989.0 2989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4452.0 6895.5 5634.0 5634.0 5634.0 5634.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 275.0 3998.5 5488.0 5792.5 5792.5 5792.5 5792.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7147.0 7314.5 3244.5 6223.5 6223.5 6448.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7301.0 6223.5 6223.5 6223.5 6223.5 6223.5 1956.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3517.0 5190.5 6223.5 2487.5 3595.0 5233.5 7031.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6389.5 1866.5 3556.0 5213.5 6379.0 416.5 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 774.0 192.0 5634.0 7212.0 1596.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5011.5 2913.0 2913.0 5170.0 4419.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 3244.5 5927.0 2765.0 5011.5 5634.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 4638.0 6223.5 6223.5 1796.0 3517.0 5792.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2940.0 4452.0 6369.0 1596.0 4226.0 1297.0 1297.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4767.5 3962.5 1596.0 4226.0 1596.0 5011.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5011.5 4226.0 5927.0 2765.0 3766.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 6223.5 6223.5 1596.0 774.0 3766.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 2487.5 5190.5 5011.5 6448.5 4226.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3766.5 5927.0 85.5 774.0 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 6223.5 7031.5 4638.0 5634.0 6448.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5011.5 5011.5 5927.0 5927.0 6840.0 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 5343.0 6223.5 6223.5 3766.5 3766.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6223.5 774.0 3244.5 5634.0 5927.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6618.5 1596.0 4226.0 5011.5 5011.5 6448.5 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5927.0 774.0 2200.5 3766.5 3766.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1866.5 4487.5 6448.5 7031.5 416.5 2200.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6448.5 2200.5 5011.5 5011.5 6840.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3766.5 5634.0 5634.0 5343.0 7031.5 5.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 5927.0 6940.5 1114.5 3766.5 3962.5 5792.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 4638.0 1596.0 3766.5 6618.5 2200.5 5213.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6940.5 7242.5 6223.5 15.5 6940.5 7242.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1866.5 2765.0 4638.0 3766.5 3766.5 7031.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1114.5 3244.5 5011.5 6738.5 6448.5 7104.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6618.5 5343.0 5343.0 5634.0 6940.5 6223.5 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3595.0 5233.5 7088.0 6618.5 7296.5 7212.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1956.5 3517.0 5190.5 2487.5 3595.0 5233.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 6448.5 1751.0 4767.5 416.5 2200.5 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 3244.5 5634.0 5634.0 928.5 2989.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 416.5 85.5 1114.5 2200.5 4638.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 4638.0 5927.0 5011.5 85.5 2200.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 774.0 1596.0 2765.0 4226.0 416.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 1596.0 5343.0 3766.5 3766.5 5343.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3244.5 4452.0 642.0 5011.5 3766.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4638.0 5343.0 6940.5 5011.5 6840.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7031.5 7212.0 15.5 6940.5 7295.0 5927.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7031.5 1114.5 2200.5 2200.5 6223.5 2765.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 6448.5 2765.0 1114.5 5011.5 3244.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 5927.0 3244.5 6840.0 5011.5 5927.0 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 5927.0 416.5 3556.0 4638.0 5011.5 15.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 6738.5 4638.0 3595.0 5233.5 7088.0 7262.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 7031.5 6223.5 1596.0 3766.5 5343.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5343.0 6840.0 7254.0 4226.0 5343.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 2765.0 5634.0 416.5 416.5 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 774.0 4226.0 774.0 2200.5 416.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 1114.5 4226.0 6223.5 2765.0 5343.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 192.0 774.0 3766.5 5343.0 416.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 7031.5 3244.5 5011.5 7254.0 1596.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 2437.5 4419.5 5770.0 3244.5 6840.0 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 1596.0 3766.5 4638.0 5343.0 5343.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5634.0 6618.5 7147.0 1114.5 3244.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 4638.0 4638.0 2200.5 1114.5 4487.5 1796.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1402.0 4487.5 5634.0 6840.0 7147.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 3244.5 6223.5 6840.0 5343.0 192.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 6223.5 4226.0 6223.5 5343.0 6738.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 6618.5 6738.5 6223.5 2765.0 5927.0 1866.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4822.5 5011.5 6840.0 1114.5 4226.0 1297.0 5170.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6940.5 35.0 3244.5 4226.0 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5634.0 4638.0 6738.5 7266.5 1114.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3998.5 2200.5 7031.5 7212.0 141.5 2200.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 1114.5 3244.5 5011.5 3595.0 6389.5 5233.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 416.5 4638.0 5927.0 7104.0 7178.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 7104.0 7178.5 6223.5 2200.5 4638.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 5634.0 7031.5 6223.5 7212.0 7212.0 7287.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 7287.5 6223.5 7212.0 7287.5 4226.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3998.5 5501.5 6558.5 2989.0 4822.5 6389.5 2437.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3935.0 5458.0 2765.0 4638.0 6840.0 1596.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 5343.0 3766.5 5343.0 6840.0 416.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6940.5 7266.5 1343.0 2487.5 305.5 3051.0 4852.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4044.0 5818.0 416.5 5011.5 1596.0 3766.5 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 3244.5 1866.5 5213.5 6223.5 2200.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3935.0 4767.5 7120.0 3244.5 6940.5 2765.0 5190.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 3556.0 5809.5 151.5 5501.5 5011.5 192.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5.0 3766.5 5792.5 4226.0 5634.0 7031.5 7277.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 4226.0 6618.5 7178.5 4452.0 6681.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 3482.5 6509.0 6223.5 1956.5 4044.0 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3517.0 5792.5 7083.0 6840.0 4226.0 5343.0 7147.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6904.0 5927.0 2200.5 4767.5 6618.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 7031.5 5011.5 6940.5 7266.5 2765.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 2200.5 3766.5 3766.5 3766.5 3766.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6448.5 6940.5 6840.0 6840.0 5634.0 7254.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6840.0 4226.0 5011.5 6223.5 5634.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6379.0 6840.0 6223.5 1596.0 3244.5 4638.0 3556.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5809.5 6904.0 2765.0 4638.0 6223.5 2765.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5011.5 6618.5 2200.5 5488.0 2552.0 6076.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2552.0 6076.0 2552.0 6076.0 192.0 2765.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4791.5 6738.5 3244.5 6223.5 4638.0 6448.5 7104.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 2940.0 5343.0 7104.0 3244.5 3244.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 7147.0 2200.5 3766.5 5927.0 6223.5 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2552.0 3556.0 6379.0 1596.0 2765.0 6223.5 252.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3962.5 774.0 3244.5 5792.5 5792.5 4452.0 5190.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6788.0 5634.0 5634.0 7147.0 416.5 7147.0 7254.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5458.0 3766.5 6448.5 4638.0 5634.0 2989.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 2200.5 4822.5 6543.0 6681.5 7147.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 3766.5 6448.5 2200.5 5927.0 416.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 416.5 1114.5 3766.5 1446.5 5476.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5927.0 3244.5 6223.5 4638.0 4638.0 7104.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 5343.0 1114.5 3244.5 4638.0 1796.0 4791.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5818.0 2765.0 5011.5 6223.5 2765.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 2765.0 4638.0 416.5 1114.5 2765.0 5476.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6788.0 7190.0 4226.0 7031.5 2200.5 1114.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3935.0 5170.0 5343.0 6940.5 6840.0 4510.5 6910.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 6940.5 6618.5 5213.5 6904.0 2765.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6051.0 6558.5 3766.5 5343.0 6448.5 5343.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6990.5 5634.0 6738.5 6223.5 6738.5 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 7212.0 6940.5 7178.5 7031.5 7277.0 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4452.0 4452.0 4791.5 6985.0 6840.0 3482.5 4822.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6525.0 1596.0 3051.0 4044.0 2913.0 3556.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7178.5 870.0 2913.0 2200.5 4487.5 5170.0 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 131.5 2487.5 2200.5 3998.5 928.5 1596.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 6223.5 5190.5 6910.0 5011.5 1866.5 1866.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1866.5 5011.5 6618.5 2200.5 7147.0 2487.5 4452.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1866.5 5011.5 2612.0 5792.5 2487.5 6448.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 2552.0 4487.5 928.5 2765.0 5501.5 6887.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3051.0 4791.5 3766.5 5792.5 2989.0 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6223.5 5011.5 4226.0 6618.5 3935.0 3935.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3935.0 3935.0 5634.0 5343.0 5927.0 6223.5 4044.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6788.0 416.5 4226.0 7075.0 7075.0 6223.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6223.5 7254.0 6618.5 7254.0 6618.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6525.0 3766.5 5343.0 6738.5 2612.0 4044.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4510.5 4638.0 5343.0 6840.0 4226.0 6618.5 3517.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5190.5 4226.0 5190.5 7083.0 4226.0 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 1596.0 2765.0 3766.5 3766.5 6738.5 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6788.0 85.5 7147.0 2552.0 3556.0 6223.5 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 4226.0 2765.0 6448.5 774.0 1596.0 1446.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3595.0 4638.0 6940.5 416.5 1114.5 6840.0 2487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 6840.0 774.0 6940.5 2765.0 4638.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 5927.0 3244.5 6223.5 6738.5 4822.5 6076.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6448.5 3244.5 7075.0 2765.0 4638.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 5343.0 7031.5 4638.0 305.5 305.5 7124.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6690.5 2552.0 7131.5 7124.5 5501.5 6357.5 7293.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6357.5 6525.0 7244.5 6690.5 7271.5 5927.0 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4487.5 4487.5 7075.0 6979.0 6681.5 3766.5 7165.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5343.0 6940.5 4419.5 5458.0 6781.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5343.0 2765.0 5927.0 1796.0 3962.5 5476.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 85.5 774.0 1596.0 275.0 2989.0 1114.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 5011.5 3595.0 6738.5 1596.0 5634.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5011.5 1114.5 6618.5 5343.0 2940.0 6525.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 3556.0 6904.0 928.5 6076.0 6076.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 3998.5 6792.5 1596.0 3766.5 416.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 1596.0 3244.5 6840.0 5634.0 6618.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7104.0 5343.0 5927.0 1114.5 3244.5 6223.5 6357.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 3766.5 6940.5 5011.5 5011.5 3766.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 5927.0 5011.5 7031.5 6223.5 6223.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 5927.0 3244.5 5011.5 6840.0 7277.0 7031.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7031.5 7031.5 5343.0 6738.5 5634.0 7031.5 7031.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 6223.5 6223.5 7178.5 3766.5 5011.5 7266.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1956.5 3556.0 5213.5 6904.0 1402.0 2989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4822.5 6673.5 4822.5 3766.5 5011.5 6223.5 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 2765.0 2200.5 416.5 2200.5 1114.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2765.0 4638.0 5011.5 774.0 774.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 416.5 3766.5 1446.5 4044.0 1114.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 2200.5 5343.0 5634.0 6618.5 6840.0 2487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3962.5 5476.0 1114.5 2940.0 4791.5 1866.5 4487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 1114.5 4638.0 4638.0 85.5 1596.0 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 2765.0 4226.0 5343.0 2765.0 5011.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 192.0 416.5 2200.5 3244.5 774.0 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3244.5 3244.5 1866.5 4822.5 774.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 5011.5 1114.5 3766.5 5927.0 35.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3766.5 2200.5 4226.0 5011.5 774.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 774.0 3766.5 6223.5 6223.5 6738.5 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7031.5 7031.5 2765.0 2765.0 4226.0 4226.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 6618.5 7287.5 5927.0 4226.0 5927.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 1596.0 4226.0 2765.0 5011.5 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 4638.0 6223.5 7287.5 416.5 1596.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 7254.0 1596.0 4226.0 5634.0 6738.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5011.5 5343.0 5343.0 5927.0 6618.5 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2552.0 4852.0 4638.0 5927.0 6840.0 6840.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 5.0 6738.5 5011.5 6223.5 4226.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 3766.5 2765.0 5343.0 3766.5 3766.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 2940.0 2940.0 6223.5 6840.0 5927.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4852.0 6558.5 5011.5 1866.5 2200.5 4226.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 3244.5 4226.0 6223.5 6940.5 5634.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5343.0 3766.5 6223.5 5343.0 5343.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 5634.0 1596.0 3766.5 5927.0 5634.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 6223.5 3766.5 5011.5 6223.5 1402.0 2989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4822.5 3595.0 5011.5 6223.5 678.5 3595.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 416.5 2200.5 1114.5 4226.0 5011.5 928.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2989.0 35.0 275.0 35.0 3766.5 5343.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 1114.5 2765.0 5343.0 6448.5 5927.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 6738.5 4638.0 1114.5 2200.5 4226.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 2765.0 1596.0 1596.0 5343.0 3244.5 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1343.0 5011.5 5011.5 3244.5 1866.5 2989.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 7147.0 4226.0 6448.5 7301.0 7308.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 5343.0 6223.5 7147.0 7308.5 4638.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 2200.5 4638.0 1114.5 6840.0 1114.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6223.5 774.0 4638.0 1596.0 4226.0 7147.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 2200.5 5634.0 5927.0 3244.5 6840.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3517.0 774.0 774.0 5927.0 35.0 898.5 898.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 15.5 4638.0 5343.0 6738.5 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6618.5 7212.0 7031.5 5634.0 6223.5 774.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 4638.0 1114.5 3244.5 4226.0 5011.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5634.0 6618.5 774.0 4226.0 1297.0 1297.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3482.5 3482.5 774.0 2765.0 2200.5 2765.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 2200.5 4226.0 2200.5 1114.5 3244.5 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 416.5 6223.5 192.0 774.0 3766.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 603.5 3517.0 3244.5 5011.5 3244.5 5011.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 1596.0 1596.0 141.5 1402.0 3556.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1114.5 7104.0 1596.0 3766.5 4638.0 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 6940.5 6223.5 7147.0 7287.5 7308.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 5343.0 1114.5 3244.5 5011.5 1114.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 4638.0 1596.0 2552.0 5213.5 85.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 774.0 1596.0 1114.5 4226.0 6223.5 7147.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 3766.5 3244.5 1596.0 2200.5 5011.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 3244.5 5634.0 6618.5 3244.5 3244.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1402.0 4487.5 3766.5 5343.0 2765.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4791.5 6051.0 2200.5 6223.5 85.5 4852.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 4226.0 4226.0 774.0 3766.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 7031.5 5213.5 5011.5 3244.5 3244.5 1866.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5011.5 5011.5 2200.5 4226.0 5634.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 6448.5 7031.5 2765.0 3244.5 6223.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7031.5 5011.5 6223.5 7031.5 7212.0 7301.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 5634.0 6840.0 5011.5 6223.5 5634.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7147.0 7254.0 5634.0 6223.5 6223.5 2200.5 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7254.0 5634.0 3595.0 5343.0 6448.5 1446.5 2487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4419.5 2552.0 3998.5 5488.0 416.5 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 3244.5 1297.0 2437.5 678.5 1956.5 4852.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 3766.5 1114.5 3244.5 6792.5 7273.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 6448.5 5927.0 2200.5 1343.0 6369.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 2765.0 1596.0 4226.0 1866.5 3998.5 3935.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 678.5 1446.5 1446.5 5233.5 5818.0 3244.5 2487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1343.0 3595.0 2200.5 1796.0 3962.5 5458.0 6781.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 1596.0 5.0 1596.0 6543.0 1596.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6738.5 1114.5 1114.5 4226.0 5488.0 4822.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6792.5 5634.0 6618.5 1114.5 3766.5 4452.0 2913.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4767.5 2200.5 2200.5 4638.0 6448.5 3766.5 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 7031.5 6940.5 3962.5 3962.5 5927.0 5458.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7031.5 6618.5 6940.5 5011.5 4226.0 3766.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7178.5 7104.0 4226.0 4791.5 6525.0 6525.0 6525.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6525.0 6525.0 7031.5 6448.5 4226.0 6840.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6509.0 6618.5 6840.0 6223.5 6840.0 5011.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6223.5 4638.0 6840.0 6223.5 2765.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 1596.0 2765.0 3766.5 1596.0 3244.5 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 958.0 4044.0 1596.0 3766.5 5634.0 898.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1866.5 3556.0 1866.5 3556.0 1866.5 3556.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 2200.5 3517.0 5011.5 603.5 3556.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 7031.5 1402.0 2765.0 1114.5 3244.5 5458.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6972.5 2765.0 5343.0 5927.0 1114.5 2765.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 4638.0 4226.0 5634.0 5011.5 3051.0 6092.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5501.5 3244.5 5343.0 2200.5 4638.0 3766.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 1596.0 5011.5 1596.0 1596.0 2765.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 2765.0 3962.5 5343.0 6940.5 4226.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5343.0 5343.0 7178.5 6379.0 7086.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3517.0 5190.5 1114.5 3244.5 1114.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 4226.0 5927.0 1114.5 2200.5 4638.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3556.0 2200.5 3766.5 5343.0 2200.5 4226.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 6448.5 1596.0 4226.0 2200.5 4226.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3244.5 5770.0 7075.0 4226.0 5927.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 4226.0 5011.5 2765.0 4638.0 2765.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 3244.5 4226.0 5634.0 1114.5 5011.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1114.5 5233.5 6092.5 2200.5 3244.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5011.5 4226.0 6223.5 5927.0 2200.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6448.5 3935.0 6681.5 2200.5 4226.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3766.5 6990.5 5634.0 4452.0 4226.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 6223.5 6840.0 5634.0 5634.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 5634.0 5634.0 5634.0 4791.5 5634.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5927.0 6895.5 1114.5 2989.0 4452.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2487.5 3962.5 3962.5 928.5 4791.5 1402.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4419.5 1114.5 3244.5 2200.5 2989.0 4452.0 958.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1796.0 3998.5 1866.5 3517.0 3517.0 4822.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2612.0 4767.5 4767.5 6223.5 7212.0 7147.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 4452.0 3935.0 5458.0 2552.0 5488.0 3962.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 3244.5 5634.0 3517.0 5488.0 642.0 2487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4791.5 6795.5 2200.5 4452.0 3766.5 4638.0 2940.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 774.0 2552.0 4226.0 5770.0 1866.5 4767.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 4226.0 4226.0 6895.5 6895.5 6895.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6895.5 5343.0 7294.0 774.0 5011.5 5927.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6887.5 4419.5 6887.5 7212.0 6618.5 3244.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 2765.0 4638.0 4638.0 1114.5 2200.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2612.0 4044.0 4510.5 416.5 2200.5 4638.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1596.0 5927.0 5634.0 2940.0 4791.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 4226.0 1114.5 3244.5 1114.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 4638.0 3766.5 85.5 7031.5 3517.0 7235.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 6223.5 2487.5 5792.5 774.0 5634.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 1114.5 3244.5 2200.5 5011.5 928.5 1866.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6076.0 5634.0 5011.5 5634.0 1114.5 7147.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6618.5 7031.5 5927.0 5927.0 6223.5 3244.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7104.0 3556.0 5011.5 4226.0 5343.0 6223.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6940.5 5343.0 4638.0 6738.5 6738.5 561.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7075.0 6525.0 5501.5 1796.0 7131.5 898.5 4419.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6379.0 7075.0 2552.0 6357.5 6979.0 6558.5 6051.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 5011.5 3962.5 4487.5 7304.0 2552.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 7165.0 2200.5 4226.0 5634.0 1114.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 1114.5 2765.0 3766.5 2765.0 5927.0 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 6448.5 85.5 5011.5 6618.5 275.0 2989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5634.0 2765.0 6223.5 2200.5 5634.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 2200.5 5634.0 6840.0 5011.5 5011.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6940.5 2765.0 1114.5 3244.5 774.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 1596.0 3766.5 3244.5 5634.0 1596.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3766.5 5927.0 416.5 1596.0 3766.5 6357.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 2200.5 5927.0 4638.0 7178.5 1114.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 5927.0 774.0 3244.5 7104.0 6618.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3766.5 2989.0 2989.0 1956.5 4044.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6223.5 6223.5 6738.5 416.5 2200.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 6738.5 7178.5 6738.5 5927.0 7312.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6223.5 6448.5 2200.5 2200.5 7031.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 7212.0 5011.5 3766.5 5011.5 6223.5 6895.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5011.5 6558.5 7031.5 7246.0 2200.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 7104.0 85.5 192.0 5634.0 85.5 192.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 35.0 774.0 2200.5 4226.0 7104.0 192.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 2200.5 1114.5 561.5 3482.5 1114.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 4638.0 5634.0 5927.0 85.5 15.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 1114.5 2765.0 4638.0 1114.5 2765.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5927.0 2200.5 2200.5 5170.0 5170.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 416.5 3766.5 4638.0 5343.0 6618.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1114.5 5634.0 774.0 774.0 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2200.5 1114.5 3766.5 4638.0 3766.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 3244.5 4226.0 6223.5 1114.5 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 6840.0 7104.0 7282.0 774.0 2200.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5343.0 6223.5 2200.5 5011.5 416.5 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 1596.0 2765.0 2765.0 3766.5 3766.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 1114.5 1114.5 6223.5 7031.5 6223.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 2989.0 2989.0 774.0 3244.5 1596.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 1114.5 3766.5 3766.5 1596.0 5343.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 7031.5 7277.0 416.5 1402.0 4487.5 192.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 2765.0 5011.5 3244.5 6940.5 6940.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 2200.5 774.0 774.0 3244.5 3244.5 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 416.5 1596.0 774.0 6223.5 1114.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5927.0 774.0 3766.5 4226.0 6223.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 2765.0 6738.5 5927.0 6223.5 2765.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 35.0 3244.5 4226.0 5343.0 1866.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 6618.5 6618.5 5927.0 5343.0 5634.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3766.5 3244.5 4226.0 3766.5 5927.0 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6940.5 5343.0 6940.5 6223.5 2200.5 4226.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7239.0 7239.0 7298.0 6223.5 1956.5 3556.0 5213.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5011.5 6558.5 1446.5 3051.0 4852.0 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 870.0 5458.0 1114.5 2765.0 1114.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 2913.0 4767.5 774.0 1596.0 1114.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 4226.0 5927.0 3766.5 5011.5 6738.5 7266.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 85.5 3244.5 416.5 5634.0 774.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 1114.5 416.5 6448.5 4638.0 192.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7031.5 3244.5 5343.0 6223.5 6618.5 5343.0 4452.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 642.0 5634.0 774.0 3244.5 3766.5 774.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 1114.5 3244.5 2765.0 2765.0 3766.5 15.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 1114.5 774.0 2200.5 4638.0 4226.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 3244.5 3766.5 3766.5 3766.5 6448.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 1114.5 5011.5 6618.5 7147.0 5927.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 6448.5 5343.0 85.5 774.0 774.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 4226.0 1114.5 4638.0 6738.5 5634.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3766.5 5927.0 5927.0 5634.0 6618.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5343.0 6448.5 7178.5 4226.0 5343.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7031.5 2200.5 3244.5 5343.0 5927.0 416.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 416.5 2200.5 2200.5 85.5 192.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 2200.5 1114.5 3244.5 5634.0 416.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 416.5 4638.0 2765.0 5927.0 1596.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6940.5 7266.5 2200.5 6223.5 1114.5 3244.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 5927.0 416.5 2200.5 416.5 2437.5 4419.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5770.0 85.5 1114.5 85.5 1596.0 774.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5343.0 2765.0 2765.0 1114.5 2200.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1114.5 3244.5 5011.5 3766.5 6448.5 7104.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 4638.0 1796.0 4487.5 6223.5 1402.0 4487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5927.0 6618.5 4226.0 5634.0 3766.5 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7178.5 5343.0 4226.0 5634.0 3244.5 1596.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5343.0 6738.5 1114.5 4226.0 6618.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 5927.0 2765.0 1751.0 4767.5 2200.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 4226.0 3482.5 5170.0 1114.5 4226.0 15.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 4226.0 4226.0 4226.0 4226.0 3244.5 3556.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5213.5 5809.5 416.5 3244.5 4452.0 5927.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 141.5 2200.5 5011.5 6840.0 2765.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 4510.5 5818.0 6697.0 1596.0 1114.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 6618.5 6840.0 774.0 2765.0 5927.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 6223.5 4638.0 6618.5 7147.0 7031.5 7254.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 7287.5 6223.5 7212.0 7212.0 7287.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 7287.5 4226.0 5927.0 1596.0 2765.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2989.0 4822.5 6389.5 3482.5 5170.0 6673.5 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2200.5 774.0 2765.0 192.0 2765.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5927.0 2200.5 6940.5 416.5 3766.5 1343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1796.0 305.5 4852.0 3051.0 4044.0 5818.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 1596.0 3244.5 5343.0 3517.0 5476.0 2940.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5792.5 3244.5 3244.5 5634.0 1446.5 2612.0 2612.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 6448.5 1343.0 3595.0 3766.5 1297.0 4419.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 151.5 4852.0 15.5 4226.0 35.0 5792.5 5792.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2200.5 5011.5 6840.0 1114.5 774.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7147.0 928.5 3998.5 416.5 3244.5 3482.5 5458.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 1956.5 4044.0 3244.5 3517.0 5792.5 7083.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 7031.5 5011.5 2765.0 6618.5 5927.0 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 6781.0 3244.5 7031.5 1596.0 4226.0 7104.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3766.5 4638.0 3766.5 5011.5 5927.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 6448.5 6448.5 6448.5 4638.0 6448.5 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 7212.0 7031.5 5011.5 6223.5 6840.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 6223.5 7104.0 6223.5 6840.0 6840.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3244.5 4638.0 1596.0 4226.0 6223.5 3556.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5213.5 6543.0 774.0 3244.5 192.0 774.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5488.0 2552.0 6076.0 2552.0 6076.0 2552.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6076.0 3962.5 6525.0 870.0 2200.5 3766.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 192.0 2200.5 3244.5 2765.0 4791.5 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3244.5 5011.5 192.0 1114.5 2765.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5343.0 416.5 416.5 1114.5 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 1114.5 2765.0 898.5 4791.5 4226.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5011.5 774.0 1596.0 3766.5 35.0 35.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 7147.0 416.5 416.5 6509.0 774.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 1596.0 6379.0 2765.0 416.5 5927.0 2552.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5213.5 4510.5 6681.5 1114.5 3244.5 5011.5 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 3766.5 2765.0 4638.0 5927.0 6448.5 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6940.5 1446.5 5476.0 3766.5 5011.5 6618.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 4638.0 1114.5 3244.5 2765.0 5343.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1114.5 2940.0 6051.0 151.5 2612.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 1596.0 4638.0 6448.5 5343.0 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5927.0 7178.5 416.5 1114.5 3244.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5927.0 1114.5 6223.5 5170.0 3935.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 3244.5 774.0 2765.0 1114.5 3244.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 898.5 2940.0 1596.0 2765.0 6448.5 1866.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 6940.5 2200.5 7104.0 5634.0 4452.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6940.5 6618.5 6940.5 7178.5 7212.0 6840.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 7031.5 6840.0 7277.0 6990.5 6990.5 6985.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4791.5 6840.0 1866.5 2913.0 4510.5 3517.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6697.0 3517.0 5213.5 6379.0 7236.0 603.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2913.0 4510.5 252.5 252.5 5634.0 4487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6558.5 3482.5 4638.0 6681.5 3517.0 6051.0 1751.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4044.0 3766.5 3556.0 5213.5 5213.5 774.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 7147.0 3556.0 6092.5 3051.0 5343.0 898.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3998.5 958.0 2612.0 3244.5 6051.0 2989.0 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3482.5 6558.5 1596.0 4044.0 3962.5 6092.5 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4510.5 5170.0 2200.5 2765.0 5213.5 6618.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3766.5 1446.5 1446.5 1446.5 1446.5 2989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2989.0 2989.0 2989.0 603.5 1446.5 1596.0 6051.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7075.0 7075.0 5170.0 3482.5 5770.0 6223.5 7254.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 7277.0 7031.5 2200.5 4226.0 6223.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5011.5 3935.0 5770.0 6972.5 416.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 416.5 2765.0 2765.0 2765.0 7178.5 1343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3517.0 6076.0 6076.0 6076.0 6076.0 2765.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 416.5 2765.0 2765.0 2765.0 192.0 7147.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1402.0 2552.0 1596.0 2765.0 1114.5 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 3766.5 5634.0 4419.5 6673.5 1596.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 1114.5 2200.5 5190.5 5634.0 1114.5 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 4226.0 6223.5 416.5 5927.0 5927.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 4638.0 5770.0 1114.5 2200.5 4226.0 7104.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5458.0 1596.0 3766.5 6223.5 5927.0 7031.5 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 305.5 305.5 6690.5 7124.5 6979.0 2552.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7124.5 7281.0 6525.0 3051.0 6051.0 6979.0 6690.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7244.5 7271.5 7031.5 5634.0 4487.5 6979.0 2552.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7233.5 6369.0 2765.0 5011.5 1114.5 2765.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2765.0 3766.5 4226.0 5927.0 6940.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 642.0 1402.0 2552.0 192.0 2200.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1796.0 5476.0 1114.5 5011.5 1114.5 3244.5 928.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1866.5 1114.5 5634.0 1114.5 3244.5 5011.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6223.5 774.0 3766.5 5343.0 1114.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2612.0 6697.0 6697.0 2200.5 5634.0 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4510.5 7088.0 2913.0 5458.0 7120.0 2765.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 3244.5 2200.5 7178.5 3998.5 6618.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 6618.5 4226.0 5770.0 1596.0 6940.5 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3766.5 6448.5 5927.0 4226.0 4226.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5011.5 5634.0 4226.0 5011.5 6840.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 6223.5 5011.5 5011.5 5011.5 5011.5 7178.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6940.5 2552.0 3556.0 3556.0 4226.0 6223.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 7178.5 5011.5 5343.0 7266.5 3766.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6840.0 2200.5 3766.5 5343.0 5343.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3595.0 5011.5 6448.5 6738.5 85.5 192.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 85.5 192.0 5011.5 5011.5 774.0 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 774.0 774.0 4226.0 5011.5 561.5 3482.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2612.0 4852.0 2200.5 3244.5 5011.5 5343.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 2765.0 3766.5 1114.5 2765.0 5343.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 4638.0 3766.5 5634.0 3766.5 3766.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 192.0 2200.5 1114.5 4638.0 3766.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 2765.0 5011.5 2765.0 4638.0 1596.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 1114.5 1114.5 1114.5 3766.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5011.5 1596.0 4226.0 2200.5 5011.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5634.0 2765.0 4226.0 5927.0 6448.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5343.0 416.5 1596.0 4226.0 6223.5 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 416.5 1596.0 1596.0 3766.5 3766.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5011.5 5011.5 1114.5 1114.5 5011.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 2765.0 4226.0 3517.0 3517.0 774.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5927.0 2200.5 2200.5 6223.5 6223.5 192.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 774.0 2765.0 4638.0 5927.0 1596.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 192.0 2200.5 2765.0 5011.5 1596.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 2200.5 5011.5 5011.5 6618.5 6618.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 4226.0 4226.0 416.5 1596.0 6448.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 6223.5 192.0 1596.0 2200.5 5011.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 6840.0 5634.0 5011.5 5927.0 5343.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5634.0 4226.0 85.5 6223.5 7031.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1866.5 3766.5 5343.0 5927.0 5927.0 6618.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 4226.0 1596.0 4226.0 4638.0 5927.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6940.5 6223.5 5634.0 5634.0 5343.0 6940.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 6223.5 5634.0 6223.5 6840.0 6223.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 6223.5 2200.5 3766.5 5343.0 1866.5 3556.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5213.5 1596.0 2765.0 2913.0 6035.0 5011.5 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 4226.0 5011.5 2913.0 4767.5 774.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 85.5 3766.5 6448.5 1596.0 4226.0 1596.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6618.5 6223.5 2200.5 5011.5 3766.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3766.5 5927.0 2765.0 416.5 5343.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 3766.5 5927.0 7031.5 1343.0 6618.5 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1866.5 2989.0 3766.5 774.0 3244.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 3766.5 5927.0 1114.5 3244.5 2765.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 7308.5 5927.0 6448.5 4638.0 2200.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 5011.5 416.5 3244.5 5927.0 6223.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 1596.0 7147.0 4226.0 2200.5 5011.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 1114.5 3244.5 5927.0 3766.5 1596.0 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6618.5 3766.5 2765.0 2765.0 4226.0 7104.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6738.5 2200.5 4226.0 6223.5 6840.0 7239.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7239.0 6223.5 2765.0 3766.5 5343.0 5343.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 4226.0 5011.5 2200.5 4226.0 5634.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 3244.5 1297.0 1297.0 3482.5 3482.5 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 2200.5 192.0 5634.0 2200.5 4226.0 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 1114.5 3244.5 416.5 2200.5 416.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 2765.0 6738.5 7104.0 2487.5 5190.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 774.0 2765.0 3766.5 416.5 2200.5 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 141.5 1402.0 4487.5 416.5 3244.5 1114.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 2765.0 3766.5 6738.5 2765.0 5927.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3244.5 5011.5 2765.0 4638.0 5343.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 5011.5 4638.0 5343.0 5343.0 5343.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2552.0 5213.5 85.5 1114.5 3766.5 774.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 4638.0 6448.5 4226.0 5011.5 6618.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 2200.5 5011.5 1114.5 3244.5 4638.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 3244.5 3244.5 5343.0 3766.5 1297.0 4419.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 2765.0 4226.0 6840.0 5476.0 6525.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 2765.0 6940.5 4226.0 4226.0 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 642.0 3556.0 5213.5 5634.0 7031.5 5476.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 3244.5 3244.5 1866.5 2200.5 5011.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 4226.0 5634.0 2765.0 4638.0 4638.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 416.5 2200.5 5011.5 5634.0 3244.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6618.5 6223.5 7301.0 7212.0 7031.5 7212.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6618.5 6840.0 7147.0 7254.0 5634.0 5634.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6223.5 2200.5 85.5 7254.0 5634.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5343.0 1446.5 2487.5 4419.5 2913.0 4767.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6357.5 416.5 1114.5 3244.5 1114.5 3244.5 1297.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2437.5 678.5 1956.5 4852.0 4226.0 6448.5 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5488.0 7129.0 1596.0 6448.5 5343.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 1343.0 6369.0 5634.0 416.5 2200.5 2552.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4822.5 3517.0 5190.5 3935.0 1446.5 2612.0 2913.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6035.0 6035.0 6448.5 2487.5 898.5 3051.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1297.0 3482.5 5458.0 6781.0 1596.0 5634.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1343.0 6543.0 1596.0 2765.0 5343.0 5927.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 4226.0 4226.0 1343.0 4452.0 2765.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 3766.5 4452.0 3482.5 5458.0 6840.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 6448.5 5343.0 5634.0 5927.0 2765.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 5927.0 6738.5 2913.0 3766.5 5927.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 4638.0 6738.5 6738.5 7212.0 7104.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4791.5 6525.0 6525.0 6525.0 6525.0 6525.0 7031.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 4226.0 6223.5 6840.0 6509.0 6618.5 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 7031.5 5011.5 6223.5 5634.0 6223.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6840.0 6223.5 2765.0 4638.0 5927.0 1596.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 1596.0 3244.5 4226.0 958.0 4044.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5634.0 898.5 2200.5 1866.5 3556.0 1866.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3556.0 1866.5 3556.0 2765.0 5927.0 416.5 1343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 603.5 3556.0 192.0 1114.5 5011.5 1402.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 1596.0 3766.5 5458.0 6972.5 774.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 1114.5 2765.0 4638.0 5343.0 4638.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5011.5 958.0 4852.0 4044.0 2200.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 4638.0 2765.0 2765.0 774.0 1596.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 1596.0 2765.0 6448.5 2765.0 2765.0 3962.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 4638.0 4226.0 6738.5 5011.5 3766.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 1596.0 3766.5 1596.0 3766.5 3517.0 5190.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 3244.5 1114.5 3244.5 3766.5 5343.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3244.5 5343.0 2200.5 3556.0 3244.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 2200.5 4226.0 3244.5 1596.0 6448.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5343.0 6448.5 1596.0 2200.5 3244.5 5458.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6781.0 3244.5 5343.0 5343.0 1114.5 4226.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 5927.0 2765.0 3244.5 5634.0 1114.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 1114.5 5011.5 7104.0 3766.5 3244.5 6092.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5233.5 2200.5 3244.5 5634.0 2200.5 5011.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5927.0 2200.5 4638.0 3766.5 5927.0 3935.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1866.5 2200.5 4226.0 5634.0 2765.0 6448.5 4452.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6990.5 5011.5 5634.0 5011.5 5011.5 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 5011.5 5011.5 5634.0 5634.0 6223.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6985.0 4791.5 5927.0 1596.0 2989.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 4226.0 6223.5 1446.5 2913.0 4419.5 4419.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 870.0 4791.5 928.5 2487.5 4044.0 192.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 2612.0 3935.0 4852.0 6076.0 7239.0 2940.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5170.0 1866.5 4419.5 5634.0 3244.5 5170.0 5170.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 898.5 1956.5 7147.0 2200.5 3766.5 5501.5 2940.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 1751.0 4638.0 2913.0 5458.0 3244.5 6904.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3556.0 5501.5 1402.0 5818.0 1596.0 3051.0 3556.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5792.5 7031.5 3556.0 3962.5 7212.0 4638.0 6690.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 5476.0 3244.5 5190.5 2765.0 2765.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 4638.0 4638.0 4638.0 4638.0 774.0 1751.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 5011.5 6076.0 6076.0 5927.0 5343.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 6618.5 7031.5 7031.5 5634.0 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 1114.5 2200.5 5634.0 2913.0 5170.0 6781.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 2200.5 4638.0 416.5 3244.5 1596.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 1796.0 3962.5 4226.0 4226.0 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 4226.0 1114.5 1114.5 3766.5 4638.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 7104.0 2487.5 7162.5 2765.0 5343.0 4452.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7083.0 774.0 5634.0 4638.0 6223.5 1114.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3244.5 928.5 1866.5 6076.0 6618.5 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 416.5 5634.0 1596.0 4638.0 7031.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6223.5 3244.5 5927.0 5343.0 4487.5 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5770.0 5634.0 1596.0 4226.0 6448.5 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 5927.0 7031.5 561.5 7075.0 5501.5 6525.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7075.0 2552.0 898.5 4419.5 6357.5 561.5 2552.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5501.5 6558.5 6979.0 6051.0 6738.5 5634.0 3962.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4487.5 6357.5 4852.0 5011.5 774.0 3766.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 4226.0 1114.5 3244.5 4638.0 1114.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 774.0 4638.0 774.0 2765.0 5343.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 6840.0 2487.5 5792.5 3244.5 6840.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 928.5 3998.5 2200.5 3244.5 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 1114.5 3244.5 5011.5 2200.5 4226.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 2200.5 774.0 4226.0 4226.0 2200.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5011.5 2612.0 6697.0 2552.0 4487.5 6379.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2765.0 4638.0 5170.0 3244.5 7031.5 5190.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6448.5 3482.5 6887.5 3244.5 5343.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 85.5 5634.0 2200.5 5927.0 6448.5 2989.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2989.0 1956.5 4044.0 4226.0 5011.5 5011.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 416.5 2200.5 4638.0 6738.5 6738.5 7178.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 5927.0 7312.0 2200.5 3244.5 3244.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 2200.5 6618.5 5011.5 5011.5 7031.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3244.5 5011.5 5011.5 3766.5 5011.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6223.5 1402.0 2989.0 4822.5 4822.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 4226.0 2200.5 774.0 4638.0 7178.5 2913.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4419.5 6357.5 6887.5 4452.0 4452.0 6051.0 4791.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 6223.5 2765.0 5011.5 5634.0 6840.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6618.5 1114.5 1114.5 3766.5 3766.5 1297.0 2913.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4767.5 2200.5 3766.5 5343.0 774.0 3244.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 6223.5 6558.5 85.5 1596.0 2765.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 1114.5 2200.5 192.0 416.5 1596.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 5634.0 6840.0 6618.5 5634.0 5634.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 6738.5 2765.0 5343.0 4226.0 5927.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 5634.0 5634.0 4226.0 5634.0 6448.5 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 774.0 1596.0 2765.0 1114.5 2765.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 6223.5 85.5 85.5 774.0 774.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 5634.0 5634.0 6448.5 6448.5 35.0 35.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 416.5 416.5 1596.0 4226.0 3244.5 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 6618.5 1596.0 4226.0 192.0 192.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 1114.5 4226.0 5011.5 5927.0 6618.5 7031.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 1343.0 3962.5 2200.5 4638.0 6223.5 7212.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2765.0 1596.0 774.0 1596.0 1596.0 5476.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4767.5 416.5 774.0 3244.5 3244.5 192.0 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3244.5 5634.0 6223.5 3244.5 5343.0 3556.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3998.5 5343.0 6738.5 5634.0 6223.5 6448.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6448.5 5809.5 5343.0 6448.5 6618.5 5011.5 5170.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5170.0 3244.5 1866.5 2765.0 4638.0 3766.5 7031.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 1114.5 774.0 2765.0 2765.0 5011.5 7031.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7212.0 416.5 5343.0 5927.0 6223.5 6223.5 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 2200.5 4226.0 5011.5 7031.5 7031.5 7147.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 1596.0 3244.5 5011.5 3766.5 5011.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1402.0 2989.0 4822.5 85.5 5634.0 2200.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 5343.0 416.5 1114.5 7031.5 3962.5 3962.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 35.0 35.0 85.5 1114.5 4226.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 1596.0 2765.0 1596.0 2200.5 1114.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 4226.0 2200.5 5011.5 6618.5 5343.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 5927.0 35.0 774.0 6618.5 3766.5 5770.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 416.5 5343.0 1866.5 642.0 5343.0 35.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 1596.0 5343.0 416.5 416.5 416.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 3766.5 4638.0 5.0 5343.0 4638.0 6738.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2765.0 85.5 416.5 2200.5 2200.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 3766.5 1596.0 4638.0 4226.0 4226.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6738.5 5343.0 1114.5 2200.5 3244.5 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 774.0 4226.0 3766.5 5927.0 5213.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 6738.5 6223.5 5343.0 1297.0 2437.5 3482.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4419.5 6618.5 5634.0 6223.5 4226.0 5927.0 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7178.5 1596.0 2765.0 5011.5 6223.5 1114.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 4226.0 774.0 4226.0 3244.5 3244.5 6223.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 85.5 35.0 3766.5 35.0 3766.5 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 4226.0 416.5 3244.5 5011.5 5343.0 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 3766.5 1114.5 2200.5 5011.5 6223.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 774.0 4226.0 416.5 5634.0 6223.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7254.0 1114.5 275.0 1866.5 3998.5 192.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 85.5 416.5 2200.5 5634.0 2200.5 85.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 416.5 1114.5 2200.5 2200.5 3244.5 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 3766.5 6448.5 7104.0 5927.0 5927.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 3766.5 1114.5 2765.0 3766.5 5927.0 6448.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 3244.5 774.0 1114.5 85.5 85.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 3244.5 15.5 4226.0 6223.5 774.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 5011.5 5011.5 5634.0 3766.5 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 3244.5 774.0 2765.0 2765.0 4638.0 141.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1402.0 4638.0 1596.0 5011.5 5170.0 4226.0 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 4226.0 1114.5 1596.0 3766.5 4638.0 192.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 2437.5 3766.5 4638.0 6738.5 141.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 6223.5 192.0 1596.0 3766.5 603.5 2487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2487.5 2765.0 6051.0 2200.5 2200.5 6223.5 6840.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 2200.5 4226.0 6618.5 2200.5 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6618.5 6618.5 6618.5 6618.5 5634.0 5634.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 5927.0 6738.5 6223.5 7212.0 7212.0 5634.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7287.5 561.5 870.0 2913.0 3482.5 5170.0 6543.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2200.5 3244.5 3244.5 5011.5 6223.5 561.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4419.5 192.0 3766.5 1596.0 3244.5 4226.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5634.0 2989.0 2989.0 416.5 416.5 870.0 870.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 870.0 870.0 3935.0 4226.0 1114.5 5927.0 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 774.0 5343.0 678.5 958.0 6738.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 5343.0 6448.5 7104.0 6448.5 4419.5 5343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6681.5 1596.0 4510.5 5233.5 141.5 642.0 15.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 416.5 5770.0 774.0 1343.0 5190.5 6369.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5792.5 5.0 3244.5 5634.0 6681.5 603.5 1796.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5927.0 2200.5 5343.0 1751.0 3935.0 5770.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 1114.5 3244.5 416.5 6940.5 3244.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2200.5 5927.0 5213.5 5011.5 5927.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 5927.0 5011.5 5634.0 5011.5 6618.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 6618.5 5770.0 5458.0 5458.0 5458.0 5458.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5458.0 5927.0 5011.5 6738.5 5634.0 6223.5 4791.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 5634.0 5634.0 5343.0 5011.5 6223.5 6940.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6979.0 6840.0 6223.5 3556.0 5213.5 6690.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 2765.0 3766.5 1596.0 3244.5 4226.0 1343.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4791.5 2765.0 3244.5 5011.5 3935.0 6509.0 3595.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6357.5 3595.0 6357.5 3595.0 6357.5 151.5 305.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1402.0 3766.5 5809.5 4767.5 6781.0 35.0 85.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 192.0 4452.0 6697.0 3998.5 5488.0 2200.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 1114.5 1596.0 3595.0 4044.0 4510.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 1343.0 1343.0 5792.5 2940.0 1796.0 6681.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3935.0 6781.0 416.5 5634.0 6076.0 6076.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4510.5 5792.5 2200.5 2200.5 5927.0 2765.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7104.0 3244.5 2765.0 1596.0 1866.5 2989.0 6051.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 416.5 416.5 131.5 603.5 4226.0 6618.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3766.5 4638.0 3766.5 6448.5 4638.0 6738.5 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3766.5 85.5 416.5 416.5 2765.0 6076.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1446.5 3051.0 4852.0 4638.0 5927.0 6379.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 85.5 35.0 416.5 1114.5 3244.5 3766.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6543.0 15.5 85.5 416.5 2200.5 5634.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 2200.5 416.5 1114.5 85.5 2765.0 5011.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2940.0 3962.5 6051.0 1114.5 2200.5 6448.5 192.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7178.5 603.5 603.5 3766.5 6448.5 1866.5 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2765.0 3244.5 4226.0 2765.0 3244.5 5011.5 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5011.5 3766.5 6558.5 928.5 2989.0 6076.0 3244.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 6543.0 6543.0 6543.0 6673.5 6887.5 6673.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6357.5 5634.0 5634.0 6357.5 5770.0 5927.0 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5927.0 6543.0 6543.0 5634.0 6985.0 6092.5 928.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2940.0 3595.0 3244.5 4487.5 6389.5 4419.5 6051.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6895.5 7083.0 2940.0 4822.5 3517.0 5233.5 6972.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 305.5 5501.5 4452.0 6509.0 1343.0 2487.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 2487.5 3998.5 1402.0 2989.0 6369.0 3998.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6389.5 6389.5 275.0 774.0 3244.5 3244.5 141.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 305.5 1956.5 4452.0 1596.0 4487.5 2200.5 3998.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 1446.5 1446.5 3051.0 4226.0 6558.5 1866.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4419.5 1751.0 2989.0 3244.5 6792.5 6357.5 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1114.5 3244.5 5011.5 4638.0 928.5 2913.0 2437.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2913.0 1751.0 1297.0 5233.5 6092.5 4044.0 2612.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 275.0 275.0 2765.0 6904.0 6092.5 6092.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6738.5 5343.0 2200.5 6618.5 4226.0 5011.5 4226.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1343.0 2487.5 4226.0 4226.0 5634.0 3244.5 1114.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2200.5 3244.5 3766.5 4638.0 3244.5 1596.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1751.0 870.0 1402.0 3556.0 1402.0 5809.5 5809.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5809.5 5809.5 5233.5 6910.0 3244.5 3244.5 6681.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5190.5 6618.5 1114.5 416.5 1596.0 774.0 1596.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 774.0 252.5 1343.0 774.0 1596.0 35.0 416.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4791.5 6788.0 2200.5 3244.5 603.5 3517.0 4452.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6223.5 3244.5 5011.5 5011.5 1114.5 35.0 774.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5.0 3051.0 5927.0 3244.5 6840.0 3766.5 870.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6035.0 6035.0 5792.5 870.0 1114.5 1596.0 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 7031.5 5927.0 4638.0 5343.0 898.5 898.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6051.0 6051.0 6379.0 4852.0 7233.5 5213.5 3482.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6558.5 1402.0 4852.0 4791.5 4791.5 6990.5 3766.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6940.5 4487.5 6887.5 23.0 6525.0 7165.0 678.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 678.5 3766.5 5011.5 5343.0 4044.0 5818.0 6795.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4226.0 5634.0 6840.0 192.0 85.5 2940.0 4791.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3962.5 1596.0 416.5 5634.0 2200.5 5634.0 15.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3244.5 1866.5 5488.0 1446.5 4510.5 233.0 6887.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 2200.5 416.5 85.5 4226.0 416.5 1751.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4419.5 5343.0 3935.0 7120.0 1114.5 4638.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1596.0 3766.5 603.5 1343.0 2200.5 4226.0 958.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1956.5 305.5 2200.5 4226.0 3244.5 5927.0 2200.5
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 416.5 3766.5 4226.0 416.5 678.5 3595.0 4638.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 35.0 85.5 3766.5 4226.0 2200.5 7031.5 5927.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5343.0 6051.0 6051.0 1114.5 416.5 4638.0 2765.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4638.0 2765.0 5343.0 5190.5 6369.0 7162.5 7191.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7129.0 6985.0 7129.0 7031.5 7031.5 4452.0 6051.0
## <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6051.0 6448.5 7301.0 5011.5 416.5 7031.5 5011.5
## <NA> <NA>
## 2200.5 7212.0rank(d2$HP)## [1] 156.0 390.0 754.5 754.5 63.5 336.0 714.0 714.0 714.0 129.0
## [11] 344.5 724.0 724.0 156.0 231.5 390.0 88.5 156.0 483.5 483.5
## [21] 88.5 447.0 786.5 786.5 23.5 23.5 305.0 669.5 88.5 483.5
## [31] 41.0 390.0 41.0 156.0 390.0 390.0 231.5 231.5 669.5 669.5
## [41] 305.0 576.0 843.5 181.0 434.5 782.0 576.0 891.0 57.0 57.0
## [51] 632.5 632.5 1005.0 1029.5 88.5 669.5 156.0 390.0 669.5 41.0
## [61] 390.0 390.0 576.0 2.5 2.5 41.0 41.0 88.5 88.5 231.5
## [71] 483.5 483.5 231.5 754.5 88.5 483.5 305.0 843.5 88.5 483.5
## [81] 843.5 11.5 88.5 305.0 305.0 576.0 754.5 843.5 231.5 483.5
## [91] 754.5 88.5 754.5 88.5 88.5 305.0 305.0 754.5 754.5 231.5
## [101] 231.5 483.5 483.5 843.5 843.5 891.0 891.0 891.0 11.5 231.5
## [111] 274.0 274.0 41.0 390.0 483.5 843.5 754.5 754.5 970.5 970.5
## [121] 23.5 231.5 23.5 156.0 390.0 390.0 41.0 390.0 801.5 23.5
## [131] 305.0 88.5 390.0 390.0 891.0 891.0 231.5 390.0 390.0 231.5
## [141] 231.5 843.5 88.5 483.5 483.5 754.5 970.5 1043.0 483.5 970.5
## [151] 970.5 23.5 305.0 156.0 754.5 23.5 390.0 88.5 231.5 576.0
## [161] 483.5 483.5 483.5 483.5 483.5 669.5 6.5 891.0 891.0 1023.0
## [171] 187.5 305.0 483.5 1023.0 483.5 483.5 483.5 41.0 576.0 23.5
## [181] 390.0 754.5 754.5 1036.0 843.5 843.5 843.5 843.5 843.5 843.5
## [191] 114.5 434.5 867.0 980.0 980.0 980.0 936.5 156.0 390.0 754.5
## [201] 63.5 336.0 714.0 231.5 483.5 801.5 41.0 801.5 390.0 936.5
## [211] 88.5 305.0 88.5 576.0 801.5 669.5 1017.5 6.5 231.5 843.5
## [221] 41.0 305.0 88.5 483.5 305.0 576.0 843.5 843.5 669.5 576.0
## [231] 936.5 576.0 843.5 41.0 305.0 669.5 305.0 23.5 669.5 483.5
## [241] 305.0 891.0 483.5 891.0 390.0 891.0 891.0 390.0 187.5 1039.0
## [251] 576.0 231.5 669.5 936.5 483.5 669.5 669.5 390.0 843.5 483.5
## [261] 576.0 576.0 6.5 754.5 754.5 305.0 390.0 843.5 88.5 390.0
## [271] 231.5 936.5 483.5 390.0 41.0 669.5 156.0 801.5 483.5 156.0
## [281] 669.5 669.5 669.5 843.5 843.5 801.5 632.5 305.0 41.0 231.5
## [291] 156.0 156.0 156.0 891.0 1044.5 843.5 1005.0 936.5 231.5 576.0
## [301] 936.5 936.5 980.0 980.0 936.5 88.5 231.5 576.0 576.0 156.0
## [311] 390.0 754.5 754.5 231.5 576.0 936.5 936.5 41.0 576.0 57.0
## [321] 57.0 714.0 714.0 156.0 231.5 390.0 231.5 390.0 88.5 390.0
## [331] 754.5 88.5 576.0 843.5 88.5 390.0 88.5 390.0 14.5 57.0
## [341] 528.0 528.0 88.5 576.0 390.0 390.0 390.0 754.5 1033.5 32.0
## [351] 434.5 1.0 451.5 789.5 963.0 624.5 1031.0 231.5 23.5 231.5
## [361] 576.0 231.5 231.5 231.5 231.5 231.5 390.0 576.0 576.0 23.5
## [371] 390.0 390.0 88.5 576.0 576.0 390.0 390.0 483.5 483.5 231.5
## [381] 576.0 936.5 156.0 576.0 576.0 1023.0 1038.0 390.0 576.0 576.0
## [391] 576.0 390.0 754.5 390.0 156.0 231.5 754.5 231.5 576.0 156.0
## [401] 669.5 669.5 632.5 632.5 843.5 843.5 231.5 996.0 123.0 447.0
## [411] 88.5 390.0 514.0 814.5 156.0 669.5 6.5 891.0 576.0 576.0
## [421] 576.0 576.0 390.0 129.0 451.5 451.5 6.5 88.5 914.0 669.5
## [431] 483.5 483.5 891.0 231.5 754.5 754.5 576.0 843.5 996.0 41.0
## [441] 305.0 305.0 936.5 123.0 156.0 483.5 891.0 891.0 88.5 390.0
## [451] 754.5 754.5 754.5 754.5 754.5 754.5 754.5 754.5 754.5 936.5
## [461] 936.5 936.5 936.5 970.5 970.5 936.5 231.5 231.5 231.5 231.5
## [471] 305.0 669.5 891.0 129.0 451.5 701.0 278.0 451.5 789.5 88.5
## [481] 305.0 801.5 344.5 724.0 51.0 705.5 156.0 390.0 754.5 88.5
## [491] 390.0 518.5 909.0 23.5 390.0 88.5 390.0 390.0 390.0 576.0
## [501] 23.5 576.0 390.0 305.0 801.5 156.0 576.0 701.0 1002.0 669.5
## [511] 843.5 1033.5 305.0 483.5 483.5 390.0 936.5 192.0 617.0 156.0
## [521] 447.0 961.0 329.0 518.5 231.5 6.5 936.5 701.0 231.5 336.0
## [531] 528.0 985.5 985.5 1025.5 88.5 576.0 576.0 528.0 985.5 88.5
## [541] 576.0 187.5 786.5 638.0 192.0 536.0 156.0 390.0 843.5 843.5
## [551] 576.0 576.0 996.0 1005.0 936.5 669.5 669.5 801.5 814.5 483.5
## [561] 483.5 669.5 996.0 801.5 528.0 528.0 390.0 156.0 576.0 231.5
## [571] 231.5 231.5 231.5 231.5 231.5 669.5 754.5 669.5 936.5 843.5
## [581] 867.0 996.0 1033.5 1033.5 1010.0 754.5 936.5 576.0 936.5 936.5
## [591] 1010.0 936.5 156.0 390.0 669.5 483.5 843.5 996.0 305.0 669.5
## [601] 891.0 156.0 390.0 156.0 483.5 801.5 114.5 451.5 231.5 669.5
## [611] 231.5 669.5 231.5 669.5 701.0 1007.0 231.5 441.5 754.5 156.0
## [621] 669.5 305.0 576.0 801.5 483.5 518.5 390.0 996.0 961.0 961.0
## [631] 669.5 801.5 970.5 231.5 669.5 970.5 1010.0 669.5 156.0 305.0
## [641] 669.5 23.5 88.5 390.0 88.5 390.0 156.0 576.0 576.0 576.0
## [651] 231.5 390.0 891.0 576.0 576.0 970.5 970.5 970.5 970.5 669.5
## [661] 231.5 576.0 231.5 483.5 624.5 57.0 57.0 336.0 282.0 638.0
## [671] 305.0 669.5 231.5 754.5 88.5 390.0 305.0 669.5 156.0 390.0
## [681] 576.0 156.0 483.5 996.0 441.5 669.5 50.0 270.5 617.0 390.0
## [691] 754.5 305.0 231.5 576.0 536.0 1003.0 305.0 936.5 1037.0 231.5
## [701] 576.0 129.0 638.0 88.5 390.0 390.0 41.0 483.5 801.5 305.0
## [711] 669.5 231.5 390.0 390.0 181.0 514.0 701.0 305.0 891.0 754.5
## [721] 231.5 754.5 989.0 989.0 156.0 483.5 705.5 344.5 822.0 156.0
## [731] 483.5 891.0 576.0 936.5 576.0 996.0 801.5 336.0 274.0 624.5
## [741] 873.0 305.0 801.5 867.0 867.0 867.0 724.0 724.0 724.0 724.0
## [751] 936.5 936.5 822.0 822.0 1017.5 1017.5 1017.5 867.0 867.0 936.5
## [761] 936.5 617.0 326.0 434.5 818.5 88.5 344.5 669.5 114.5 282.0
## [771] 624.5 624.5 57.0 801.5 156.0 441.5 714.0 57.0 156.0 754.5
## [781] 441.5 814.5 129.0 282.0 714.0 514.0 1014.5 518.5 891.0 669.5
## [791] 441.5 638.0 638.0 156.0 344.5 390.0 390.0 714.0 959.0 441.5
## [801] 783.5 278.0 814.5 118.5 624.5 231.5 483.5 231.5 617.0 129.0
## [811] 441.5 336.0 783.5 705.5 1014.5 891.0 714.0 518.5 231.5 156.0
## [821] 528.0 843.5 329.0 123.0 801.5 192.0 129.0 282.0 344.5 483.5
## [831] 305.0 669.5 801.5 305.0 891.0 88.5 801.5 1020.5 1020.5 985.5
## [841] 282.0 1041.0 231.5 231.5 754.5 754.5 754.5 528.0 714.0 714.0
## [851] 156.0 483.5 891.0 231.5 390.0 754.5 41.0 305.0 754.5 187.5
## [861] 818.5 183.5 329.0 705.5 183.5 909.0 669.5 669.5 669.5 669.5
## [871] 88.5 390.0 156.0 156.0 669.5 801.5 669.5 156.0 156.0 231.5
## [881] 231.5 576.0 936.5 57.0 528.0 88.5 576.0 88.5 390.0 187.5
## [891] 528.0 576.0 1010.0 118.5 274.0 624.5 270.5 843.5 936.5 11.5
## [901] 669.5 305.0 801.5 305.0 891.0 891.0 390.0 390.0 483.5 390.0
## [911] 483.5 305.0 528.0 714.0 576.0 156.0 305.0 669.5 576.0 576.0
## [921] 576.0 576.0 123.0 123.0 1027.5 1027.5 989.0 983.0 617.0 786.5
## [931] 909.0 344.5 1042.0 909.0 909.0 909.0 909.0 754.5 843.5 518.5
## [941] 632.5 434.5 278.0 818.5 181.0 1025.5 231.5 576.0 936.5 231.5
## [951] 483.5 754.5 231.5 483.5 576.0 576.0 1010.0 57.0 528.0 913.0
## [961] 11.5 231.5 390.0 88.5 576.0 88.5 390.0 118.5 624.5 231.5
## [971] 843.5 344.5 536.0 23.5 754.5 996.0 88.5 576.0 996.0 274.0
## [981] 624.5 576.0 114.5 434.5 88.5 669.5 669.5 231.5 936.5 231.5
## [991] 754.5 88.5 390.0 118.5 329.0 329.0 156.0 483.5 891.0 876.0
## [1001] 576.0 390.0 441.5 754.5 336.0 156.0 483.5 483.5 187.5 23.5
## [1011] 576.0 936.5 669.5 669.5 390.0 576.0 336.0 336.0 624.5 1013.0
## [1021] 843.5 843.5 843.5 843.5 576.0 14.5 528.0 818.5 873.0 873.0
## [1031] 873.0 873.0 1029.5 1044.5 390.0 936.5 936.5 970.5 754.5 1040.0
## [1041] 936.5 936.5 936.5 936.5 936.5head(d2[5])with(d2, sum(Defence))## [1] 78021sd(d2$HP)## [1] 26.67141Summarizing rows and columns:
mad(d2$Defence)## [1] 29.652apply(head(d2),1, mean, na.rm=TRUE)## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA## 1 2 3 4 5 6
## NA NA NA NA NA NAapply(head(d2),1, median, na.rm=TRUE)## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA## 1 2 3 4 5 6
## NA NA NA NA NA NAis.table(d2)## [1] FALSEaddmargins(head(d2),1, mean)## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA## Name Total HP Attack Defence Sp_attack
## 1 character,6 integer,6 integer,6 integer,6 character,6 integer,6
## 2 integer,6 integer,6 integer,6 integer,6 integer,6 integer,6
## 3 integer,6 character,6 integer,6 integer,6 integer,6 character,6
## 4 integer,6 integer,6 integer,6 integer,6 integer,6 integer,6
## 5 integer,6 integer,6 character,6 integer,6 integer,6 integer,6
## 6 integer,6 integer,6 integer,6 integer,6 integer,6 integer,6
## mean NA NA NA NA NA NA
## Sp_defence Speed
## 1 integer,6 integer,6
## 2 integer,6 integer,6
## 3 integer,6 integer,6
## 4 integer,6 integer,6
## 5 character,6 integer,6
## 6 integer,6 integer,6
## mean NA NAaddmargins(head(d2),1, median)## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
## is not numeric or logical: returning NA## Name Total HP Attack Defence Sp_attack
## 1 character,6 integer,6 integer,6 integer,6 character,6 integer,6
## 2 integer,6 integer,6 integer,6 integer,6 integer,6 integer,6
## 3 integer,6 character,6 integer,6 integer,6 integer,6 character,6
## 4 integer,6 integer,6 integer,6 integer,6 integer,6 integer,6
## 5 integer,6 integer,6 character,6 integer,6 integer,6 integer,6
## 6 integer,6 integer,6 integer,6 integer,6 integer,6 integer,6
## median NA NA NA NA NA NA
## Sp_defence Speed
## 1 integer,6 integer,6
## 2 integer,6 integer,6
## 3 integer,6 integer,6
## 4 integer,6 integer,6
## 5 character,6 integer,6
## 6 integer,6 integer,6
## median NA NAView(head(d2))
nrow((d2))## [1] 1045ncol(d2)## [1] 8sum(is.na(d2))## [1] 0structure(head(d2))table(head((d2$Speed)))##
## 45 60 65 80
## 1 1 1 3plot(density(d2$HP))
Drawing distribution and Graphics:
hist(d2$HP, freq = F, col= 'blue')
lines(density(d2$HP), lty= 2)
lines(density(d2$HP, k='rectangular'))
rnorm(20, mean=5, sd= 1)## [1] 6.245765 4.626042 4.520046 4.514566 5.927319 6.301246 2.593641 5.759398
## [9] 5.981533 4.831372 4.678042 5.047421 5.289058 5.060538 5.263293 3.713043
## [17] 6.305507 4.501304 7.100012 3.677507pnorm(20, mean=5, sd= 1)## [1] 1qnorm(0.5, 5, 1)## [1] 5dnorm(c(7,8,9),mean=5, sd=1)## [1] 0.0539909665 0.0044318484 0.0001338302qnorm(c(3.5,0.75),mean=5, sd=1)## Warning in qnorm(c(3.5, 0.75), mean = 5, sd = 1): NaNs produced## [1] NaN 5.67449d2.norm=rnorm(1000,mean(d2$HP), sd(d2$HP))
head(d2.norm)## [1] 3.433238 71.627281 80.308037 53.819142 91.454385 36.789027hist(d2$HP, freq = F, col= 'pink')
lines(density(d2.norm))
hist(d2.norm, freq = F, col= 'pink')
lines(density(d2$HP))
hist(d2.norm,freq = F,border = 'red',col='blue',main = 'comparing two distributions',xlab = 'data size classes')
lines(density(d2$HP), lwd= 2)
qqnorm(d2$HP)
qqnorm(d2$HP, main = 'QQ plot of example data', xlab = 'Theoretical',ylab = 'Quantiles for d2')
qqline(d2$HP, lwd = 2, lty = 2)
qqp = qqplot(d2$HP, rnorm(50,5,2))
qqp## $x
## [1] 1.00000 30.00000 35.00000 39.91837 40.00000 40.00000 44.00000
## [8] 45.00000 45.00000 49.00000 50.00000 50.00000 50.00000 53.00000
## [15] 55.00000 55.00000 59.00000 60.00000 60.00000 60.00000 60.00000
## [22] 63.42857 65.00000 65.00000 65.34694 68.00000 70.00000 70.00000
## [29] 70.00000 71.00000 74.18367 75.00000 75.00000 77.00000 79.00000
## [36] 80.00000 80.00000 84.00000 85.00000 90.00000 90.00000 92.00000
## [43] 95.00000 100.00000 100.00000 102.55102 106.00000 112.16327 130.00000
## [50] 255.00000
##
## $y
## [1] 0.8725173 1.2141105 1.5190249 2.2361928 2.2758667 2.2897934 2.4174826
## [8] 2.5086645 2.8454379 2.8997559 2.9381949 3.0573228 3.2976195 3.3351876
## [15] 3.6054571 3.8733521 4.0246310 4.0515806 4.2754768 4.3279907 4.4082356
## [22] 4.6557145 4.7224334 4.8894435 4.9479609 5.0230373 5.0250852 5.0476994
## [29] 5.4301908 5.4598904 5.5486123 5.5859593 5.7391962 5.7503352 5.7591449
## [36] 5.7616949 5.8550595 5.9131315 6.0106015 6.0210990 6.3573668 6.5285704
## [43] 6.5909674 6.7343640 6.7453835 7.6630885 7.7153135 8.1931636 8.3480016
## [50] 9.9125209abline(lm(qqp$y ~ qqp$x))
Testing distribution:
sample(head(d2),size = 4)set.seed(4)
sample(head(df), size = 3)## [1] if (missing(ncp))
## [2] }
## [3] else .Call(C_dnf, x, df1, df2, ncp, log)shapiro.test(d2$HP)##
## Shapiro-Wilk normality test
##
## data: d2$HP
## W = 0.89348, p-value < 2.2e-16ks.test(d2$HP, 'pnorm',mean=5,sd=2)## Warning in ks.test(d2$HP, "pnorm", mean = 5, sd = 2): ties should not be present
## for the Kolmogorov-Smirnov test##
## One-sample Kolmogorov-Smirnov test
##
## data: d2$HP
## D = 0.99713, p-value < 2.2e-16
## alternative hypothesis: two-sidedks.test(d2$HP, pnorm(20,5,2))## Warning in ks.test(d2$HP, pnorm(20, 5, 2)): cannot compute exact p-value with
## ties##
## Two-sample Kolmogorov-Smirnov test
##
## data: d2$HP and pnorm(20, 5, 2)
## D = 1, p-value = 0.2705
## alternative hypothesis: two-sidedT-test:
t.test(d2$HP, d2$Attack)##
## Welch Two Sample t-test
##
## data: d2$HP and d2$Attack
## t = -8.0084, df = 2013.4, p-value = 1.946e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.945611 -7.852475
## sample estimates:
## mean of x mean of y
## 70.06794 80.46699t.test(d2$HP, d2$Attack, var.equal = TRUE)##
## Two Sample t-test
##
## data: d2$HP and d2$Attack
## t = -8.0084, df = 2088, p-value = 1.911e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.94556 -7.85253
## sample estimates:
## mean of x mean of y
## 70.06794 80.46699t.test(d2$HP, mu = 10)##
## One Sample t-test
##
## data: d2$HP
## t = 72.804, df = 1044, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 10
## 95 percent confidence interval:
## 68.44897 71.68692
## sample estimates:
## mean of x
## 70.06794t.test(d2$HP, mu = 1000, alternative = 'greater')##
## One Sample t-test
##
## data: d2$HP
## t = -1127.1, df = 1044, p-value = 1
## alternative hypothesis: true mean is greater than 1000
## 95 percent confidence interval:
## 68.70963 Inf
## sample estimates:
## mean of x
## 70.06794U-test:
wilcox.test(d2$HP, d2$Attack)##
## Wilcoxon rank sum test with continuity correction
##
## data: d2$HP and d2$Attack
## W = 437371, p-value = 3.245e-15
## alternative hypothesis: true location shift is not equal to 0wilcox.test(d2$HP, exact = FALSE)##
## Wilcoxon signed rank test with continuity correction
##
## data: d2$HP
## V = 546535, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 0wilcox.test(d2$HP, mu = 8, exact = FALSE, conf.int = TRUE, alt = 'less')##
## Wilcoxon signed rank test with continuity correction
##
## data: d2$HP
## V = 546532, p-value = 1
## alternative hypothesis: true location is less than 8
## 95 percent confidence interval:
## -Inf 70
## sample estimates:
## (pseudo)median
## 67.5Correlation and Covariance:
cor(d2$Defence, d2$Attack)## [1] 0.4576705cor(d2$Attack, d2$Defence, method = 'spearman')## [1] 0.5167943cor(d2$Attack, d2$Defence, method = 'kendall')## [1] 0.3738551cov(d2$Attack, d2$Defence)## [1] 463.4074Correlation hypothesis tests:
cor.test(d2$Attack, d2$Defence)##
## Pearson's product-moment correlation
##
## data: d2$Attack and d2$Defence
## t = 16.624, df = 1043, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4083614 0.5043165
## sample estimates:
## cor
## 0.4576705Association tests:
chisq.test(d2$Attack)##
## Chi-squared test for given probabilities
##
## data: d2$Attack
## X-squared = 13631, df = 1044, p-value < 2.2e-16chisq.test(d2$HP, correct = FALSE)##
## Chi-squared test for given probabilities
##
## data: d2$HP
## X-squared = 10599, df = 1044, p-value < 2.2e-16d2.cs = chisq.test(d2$Attack, p = d2$Defence, rescale.p = TRUE)
d2.cs##
## Chi-squared test for given probabilities
##
## data: d2$Attack
## X-squared = 14951, df = 1044, p-value < 2.2e-16head(d2.cs$exp)## [1] 52.81029 67.89895 89.45417 132.56462 46.34373 62.51014names(d2.cs$expected) = row.names(d2)
head(d2.cs$exp)## 1 2 3 4 5 6
## 52.81029 67.89895 89.45417 132.56462 46.34373 62.51014(honey.cs = chisq.test(d2$Attack))##
## Chi-squared test for given probabilities
##
## data: d2$Attack
## X-squared = 13631, df = 1044, p-value < 2.2e-16names(honey.cs)## [1] "statistic" "parameter" "p.value" "method" "data.name" "observed"
## [7] "expected" "residuals" "stdres"(honey.cs = chisq.test(d2$Attack, simulate.p.value = TRUE, B = 3000))##
## Chi-squared test for given probabilities with simulated p-value (based
## on 3000 replicates)
##
## data: d2$Attack
## X-squared = 13631, df = NA, p-value = 0.0003332d2[1:2, 4:5]chisq.test(d2[1:2, 4:5], correct = FALSE)##
## Pearson's Chi-squared test
##
## data: d2[1:2, 4:5]
## X-squared = 0.0035158, df = 1, p-value = 0.9527chisq.test(d2[1:2, 4:5], correct = TRUE)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: d2[1:2, 4:5]
## X-squared = 0, df = 1, p-value = 1with(d2, chisq.test(Attack, p = Defence, rescale = T))##
## Chi-squared test for given probabilities
##
## data: Attack
## X-squared = 14951, df = 1044, p-value < 2.2e-16with(d2, chisq.test(Attack, p =Defence , rescale = T, sim = T))##
## Chi-squared test for given probabilities with simulated p-value (based
## on 2000 replicates)
##
## data: Attack
## X-squared = 14951, df = NA, p-value = 0.0004998chisq.test(d2$Attack)##
## Chi-squared test for given probabilities
##
## data: d2$Attack
## X-squared = 13631, df = 1044, p-value < 2.2e-16Box-whisker plots:
boxplot(d2$Attack)
boxplot(d2$Attack, d2$Defence)
boxplot(d2$Attack, d2$Defence, names = c('Attack', 'Defence'))
title(xlab = 'Variable', ylab = 'Value')
boxplot(d2$Attack, d2$Defence, names = c('Attack', 'Defence'), range = 0,
xlab = 'Variable', ylab = 'Value', col = 'yellow')
boxplot(d2$Attack, d2$Defence, d2$HP, names = c('Attack', 'Defence', 'HP'),
range = 0, xlab = 'variable', ylab = 'value', col = 'pink')
Scatter plots:
plot(d2$Attack, d2$Defence, pch = "+")
plot(d2$Attack, d2$Defence, xlab = 'Attack', ylab = 'Defence', pch = 10, cex = 2, col = 'blue', xlim = c(0, 500), ylim = c(0, 500))
plot(Defence ~ Attack, data = d2)
abline(lm(Defence ~ Attack, data = d2))
plot(Defence ~ Attack, data = d2)
abline(lm(d2$Attack ~ d2$Defence, data = d2), lty = 'dotted', lwd = 2, col = 'green')
plot(d2$Attack ~ d2$Defence, data = d2, xlab = 'Attack', ylab = 'Defence',
pch = 10, cex = 2, col = 'gray50', xlim = c(0, 500), ylim = c(0, 500))
abline(lm(d2$Attack ~ d2$Defence, data = d2), lty = 'dotted', lwd = 2, col = 'blue')
plot(d2$Attack, type = 'l')
with(d2, plot(Attack, HP, type = 'c'))
with(d2[order(d2$Attack),], plot(sort(Attack), HP, type = 'c'))
plot(d2$Attack, type = 'b')
Multiple correlation plots:
pairs(~ Defence + Attack + HP, data = d2)
pairs(~ d2$Attack + d2$HP + d2$Defence, data = d2, col ='brown', cex = 5, pch = 'X')
Pie charts:
pie(d2$HP, labels = d2$Total)
pc = c('gray40', 'gray50', 'gray60', 'gray70', 'gray80', 'gray90')
pie(head(d2$HP, labels = d2$Attack, col = pc, clockwise = TRUE, init.angle = 180))
pc = c('gray65', 'gray70', 'gray75', 'gray80', 'gray85', 'gray90')
pie(head(d2$Attack, labels = row.names(d2), col = pc, cex = 1.2))
d2[1,]Cleveland dot charts:
dotchart(d2$Attack)
dotchart(t(d2$HP))
#Bar charts:Bar charts:
barplot(d2$Attack)
barplot(head(d2$Defence))
barplot(head(d2$Attack))
title(xlab = 'variable', ylab = 'value')
barplot(head(d2$Attack), xlab = 'variable', ylab = 'value', ylim = c(0,500),col = 'lightblue')
abline(h = seq(1,9,2), lty = 2, lwd = 0.5, col = 'gray40')
box()
table(head(d2$Defence))##
## 43 49 58 63 83 123
## 1 1 1 1 1 1barplot(table(head(d2$Attack)), ylab = 'variable', xlab = 'value')
abline(h = 0)
barplot(head(d2$Attack, beside = TRUE, ylab = 'variable', xlab = 'value'))
barplot(t(head(d2$Attack)), beside = TRUE, legend = TRUE, cex.names = 0.8,col = c('black', 'pink', 'lightblue', 'tan', 'red', 'brown'))
title(ylab = 'variable', xlab = 'value')
bccol = c('black', 'pink', 'lightblue', 'tan', 'red', 'brown')
barplot(head(d2$Attack), beside = TRUE, legend = TRUE, horiz = TRUE,xlim = c(0, 60), col = bccol)
title(ylab = 'variable', xlab = 'vaule')
Analysis of variance (ANOVA):
bfs.aov = aov(d2$Attack ~ d2$Defence, data = d2)
bfs.aov## Call:
## aov(formula = d2$Attack ~ d2$Defence, data = d2)
##
## Terms:
## d2$Defence Residuals
## Sum of Squares 229753.8 867120.3
## Deg. of Freedom 1 1043
##
## Residual standard error: 28.83351
## Estimated effects may be unbalancedsummary(bfs.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## d2$Defence 1 229754 229754 276.4 <2e-16 ***
## Residuals 1043 867120 831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model.tables(bfs.aov, type = 'means')## Warning in replications(paste("~", xx), data = mf): non-factors ignored:
## d2$Defence## Tables of means
## Grand mean
##
## 80.46699
##
## d2$Defence
## d2$Defence
## 5 10 15 20 23 25 28 30 31 32 33
## 47.39 49.76 52.13 54.51 55.93 56.88 58.31 59.26 59.73 60.21 60.68
## 34 35 37 38 39 40 41 42 43 44 45
## 61.16 61.63 62.58 63.06 63.53 64.01 64.48 64.96 65.43 65.91 66.38
## 47 48 49 50 51 52 53 54 55 56 57
## 67.33 67.81 68.28 68.76 69.23 69.71 70.18 70.66 71.13 71.60 72.08
## 58 59 60 61 62 63 64 65 66 67 68
## 72.55 73.03 73.50 73.98 74.45 74.93 75.40 75.88 76.35 76.83 77.30
## 69 70 71 72 73 74 75 76 77 78 79
## 77.78 78.25 78.73 79.20 79.68 80.15 80.63 81.10 81.58 82.05 82.53
## 80 82 83 84 85 86 87 88 89 90 91
## 83.00 83.95 84.43 84.90 85.38 85.85 86.33 86.80 87.28 87.75 88.23
## 92 94 95 97 98 99 100 101 102 103 105
## 88.70 89.65 90.13 91.08 91.55 92.03 92.50 92.98 93.45 93.92 94.87
## 106 107 108 109 110 111 112 115 116 118 119
## 95.35 95.82 96.30 96.77 97.25 97.72 98.20 99.62 100.10 101.05 101.52
## 120 121 122 123 125 126 127 129 130 131 133
## 102.00 102.47 102.95 103.42 104.37 104.85 105.32 106.27 106.75 107.22 108.17
## 135 139 140 143 145 150 152 160 168 180 184
## 109.12 111.02 111.50 112.92 113.87 116.25 117.19 120.99 124.79 130.49 132.39
## 200 211 230 250
## 139.99 145.21 154.24 163.73pw.aov = aov(d2$Attack ~ d2$Defence+ d2$Sp_attack, data = d2)
pw.aov = aov(d2$Attack ~d2$Defence * d2$Defence, data = d2)
pw.aov## Call:
## aov(formula = d2$Attack ~ d2$Defence * d2$Defence, data = d2)
##
## Terms:
## d2$Defence Residuals
## Sum of Squares 229753.8 867120.3
## Deg. of Freedom 1 1043
##
## Residual standard error: 28.83351
## Estimated effects may be unbalancedpw.aov = aov(d2$Attack ~ d2$Defence + d2$Sp_attack + Attack:Defence, data = d2)
pw.aov## Call:
## aov(formula = d2$Attack ~ d2$Defence + d2$Sp_attack + Attack:Defence,
## data = d2)
##
## Terms:
## d2$Defence d2$Sp_attack Attack:Defence Residuals
## Sum of Squares 229753.8 81979.6 607831.2 177309.5
## Deg. of Freedom 1 1 1 1041
##
## Residual standard error: 13.05091
## Estimated effects may be unbalancedsummary(pw.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## d2$Defence 1 229754 229754 1348.9 <2e-16 ***
## d2$Sp_attack 1 81980 81980 481.3 <2e-16 ***
## Attack:Defence 1 607831 607831 3568.6 <2e-16 ***
## Residuals 1041 177310 170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pw.aov = aov(d2$Attack ~ d2$Defence * d2$Sp_attack, data = d2)
boxplot(d2$Attack ~ d2$Defence * d2$Sp_attack, data = d2, cex.axis =0.9)
title(xlab = 'varaible', ylab = 'value')
Interaction plots:
interaction.plot(d2$Attack, d2$Defence, d2$Sp_attack)
attach(d2)## The following objects are masked from head(d2):
##
## Attack, Defence, HP, Name, Sp_attack, Sp_defence, Speed, Totalinteraction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b')
detach(d2)
interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b', pch = 3:1)
interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b', pch = 3:1, lty = 3:1,
col = c('red', 'blue', 'darkgreen'))
interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b', pch = 3:1, fun = median)
interaction.plot(d2$HP, d2$Attack, d2$Defence, type = 'b', pch = 3:1,
xlab = 'variable', ylab = 'value',
main = 'Interaction plot')
head(replications(d2))## Warning in replications(d2): non-factors ignored: Total## Warning in replications(d2): non-factors ignored: HP## Warning in replications(d2): non-factors ignored: Attack## Warning in replications(d2): non-factors ignored: Defence## Warning in replications(d2): non-factors ignored: Sp_attack## Warning in replications(d2): non-factors ignored: Sp_defence## Warning in replications(d2): non-factors ignored: Speed## $Name
## Name
## Abomasnow Abra Absol Accelgor
## 1 1 1 1
## Aegislash Aerodactyl Aggron Aipom
## 1 1 1 1
## Alakazam Alcremie Alomomola Altaria
## 1 1 1 1
## Amaura Ambipom Amoonguss Ampharos
## 1 1 1 1
## Anorith Appletun Applin Araquanid
## 1 1 1 1
## Arbok Arcanine Arceus Archen
## 1 1 1 1
## Archeops Arctovish Arctozolt Ariados
## 1 1 1 1
## Armaldo Aromatisse Aron Arrokuda
## 1 1 1 1
## Articuno Audino Aurorus Avalugg
## 1 1 1 1
## Axew Azelf Azumarill Azurill
## 1 1 1 1
## Bagon Baltoy Banette Barbaracle
## 1 1 1 1
## Barboach Barraskewda Basculin Bastiodon
## 1 1 1 1
## Bayleef Beartic Beautifly Beedrill
## 1 1 1 1
## Beheeyem Beldum Bellossom Bellsprout
## 1 1 1 1
## Bergmite Bewear Bibarel Bidoof
## 1 1 1 1
## Binacle Bisharp Blacephalon Blastoise
## 1 1 1 1
## Blaziken Blipbug Blissey Blitzle
## 1 1 1 1
## Boldore Boltund Bonsly Bouffalant
## 1 1 1 1
## Bounsweet Braixen Braviary Breloom
## 1 1 1 1
## Brionne Bronzong Bronzor Bruxish
## 1 1 1 1
## Budew Buizel Bulbasaur Buneary
## 1 1 1 1
## Bunnelby Burmy Butterfree Buzzwole
## 1 1 1 1
## Cacnea Cacturne Calyrex Camerupt
## 1 1 1 1
## Carbink Carkol Carnivine Carracosta
## 1 1 1 1
## Carvanha Cascoon Castform Caterpie
## 1 1 1 1
## Celebi Celesteela Centiskorch Chandelure
## 1 1 1 1
## Chansey Charizard Charjabug Charmander
## 1 1 1 1
## Charmeleon Chatot Cherrim Cherubi
## 1 1 1 1
## Chesnaught Chespin Chewtle Chikorita
## 1 1 1 1
## Chimchar Chimecho Chinchou Chingling
## 1 1 1 1
## Cinccino Cinderace Clamperl Clauncher
## 1 1 1 1
## Clawitzer Claydol Clefable Clefairy
## 1 1 1 1
## Cleffa Clobbopus Cloyster Coalossal
## 1 1 1 1
## Cobalion Cofagrigus Combee Combusken
## 1 1 1 1
## Comfey Conkeldurr Copperajah Corphish
## 1 1 1 1
## Corsola Corviknight Corvisquire Cosmoem
## 1 1 1 1
## Cosmog Cottonee Crabominable Crabrawler
## 1 1 1 1
## Cradily Cramorant Cranidos Crawdaunt
## 1 1 1 1
## Cresselia Croagunk Crobat Croconaw
## 1 1 1 1
## Crustle Cryogonal Cubchoo Cubone
## 1 1 1 1
## Cufant Cursola Cutiefly Cyndaquil
## 1 1 1 1
## Darkrai Darmanitan Dartrix Darumaka
## 1 1 1 1
## Decidueye Dedenne Deerling Deino
## 1 1 1 1
## Delcatty Delibird Delphox Deoxys
## 1 1 1 1
## Dewgong Dewott Dewpider Dhelmise
## 1 1 1 1
## Dialga Diancie Diggersby Diglett
## 1 1 1 1
## Ditto Dodrio Doduo Donphan
## 1 1 1 1
## Dottler Doublade Dracovish Dracozolt
## 1 1 1 1
## Dragalge Dragapult Dragonair Dragonite
## 1 1 1 1
## Drakloak Drampa Drapion Dratini
## 1 1 1 1
## Drednaw Dreepy Drifblim Drifloon
## 1 1 1 1
## Drilbur Drizzile Drowzee Druddigon
## 1 1 1 1
## Dubwool Ducklett Dugtrio Dunsparce
## 1 1 1 1
## Duosion Duraludon Durant Dusclops
## 1 1 1 1
## Dusknoir Duskull Dustox Dwebble
## 1 1 1 1
## Eelektrik Eelektross Eevee Eiscue
## 1 1 1 1
## Ekans Eldegoss Electabuzz Electivire
## 1 1 1 1
## Electrike Electrode Elekid Elgyem
## 1 1 1 1
## Emboar Emolga Empoleon Entei
## 1 1 1 1
## Escavalier Espeon Espurr Eternatus
## 1 1 1 1
## Excadrill Exeggcute Exeggutor Exploud
## 1 1 1 1
## Falinks Farfetch'd Fearow Feebas
## 1 1 1 1
## Fennekin Feraligatr Ferroseed Ferrothorn
## 1 1 1 1
## Finneon Flaaffy Flabébé Flapple
## 1 1 1 1
## Flareon Fletchinder Fletchling Floatzel
## 1 1 1 1
## Floette Florges Flygon Fomantis
## 1 1 1 1
## Foongus Forretress Fraxure Frillish
## 1 1 1 1
## Froakie Frogadier Froslass Frosmoth
## 1 1 1 1
## Furfrou Furret Gabite Gallade
## 1 1 1 1
## Galvantula Garbodor Garchomp Gardevoir
## 1 1 1 1
## Gastly Gastrodon Genesect Gengar
## 1 1 1 1
## Geodude Gible Gigalith Girafarig
## 1 1 1 1
## Giratina Glaceon Glalie Glameow
## 1 1 1 1
## Glastrier Gligar Gliscor Gloom
## 1 1 1 1
## Gogoat Golbat Goldeen Golduck
## 1 1 1 1
## Golem Golett Golisopod Golurk
## 1 1 1 1
## Goodra Goomy Gorebyss Gossifleur
## 1 1 1 1
## Gothita Gothitelle Gothorita Gourgeist
## 1 1 1 1
## Granbull Grapploct Graveler Greedent
## 1 1 1 1
## Greninja Grimer Grimmsnarl Grookey
## 1 1 1 1
## Grotle Groudon Grovyle Growlithe
## 1 1 1 1
## Grubbin Grumpig Gulpin Gumshoos
## 1 1 1 1
## Gurdurr Guzzlord Gyarados Hakamo-o
## 1 1 1 1
## Happiny Hariyama Hatenna Hatterene
## 1 1 1 1
## Hattrem Haunter Hawlucha Haxorus
## 1 1 1 1
## Heatmor Heatran Heliolisk Helioptile
## 1 1 1 1
## Heracross Herdier Hippopotas Hippowdon
## 1 1 1 1
## Hitmonchan Hitmonlee Hitmontop Ho-oh
## 1 1 1 1
## Honchkrow Honedge Hoopa Hoothoot
## 1 1 1 1
## Hoppip Horsea Houndoom Houndour
## 1 1 1 1
## Huntail Hydreigon Hypno Igglybuff
## 1 1 1 1
## Illumise Impidimp Incineroar Indeedee
## 1 1 1 1
## Infernape Inkay Inteleon Ivysaur
## 1 1 1 1
## Jangmo-o Jellicent Jigglypuff Jirachi
## 1 1 1 1
## Jolteon Joltik Jumpluff Jynx
## 1 1 1 1
## Kabuto Kabutops Kadabra Kakuna
## 1 1 1 1
## Kangaskhan Karrablast Kartana Kecleon
## 1 1 1 1
## Keldeo Kingdra Kingler Kirlia
## 1 1 1 1
## Klang Klefki Klink Klinklang
## 1 1 1 1
## Koffing Komala Kommo-o Krabby
## 1 1 1 1
## Kricketot Kricketune Krokorok Krookodile
## 1 1 1 1
## Kubfu Kyogre Kyurem Lairon
## 1 1 1 1
## Lampent Landorus Lanturn Lapras
## 1 1 1 1
## Larvesta Larvitar Latias Latios
## 1 1 1 1
## Leafeon Leavanny Ledian Ledyba
## 1 1 1 1
## Lickilicky Lickitung Liepard Lileep
## 1 1 1 1
## Lilligant Lillipup Linoone Litleo
## 1 1 1 1
## Litten Litwick Lombre Lopunny
## 1 1 1 1
## Lotad Loudred Lucario Ludicolo
## 1 1 1 1
## Lugia Lumineon Lunala Lunatone
## 1 1 1 1
## Lurantis Luvdisc Luxio Luxray
## 1 1 1 1
## Lycanroc Machamp Machoke Machop
## 1 1 1 1
## Magby Magcargo Magearna Magikarp
## 1 1 1 1
## Magmar Magmortar Magnemite Magneton
## 1 1 1 1
## Magnezone Makuhita Malamar Mamoswine
## 1 1 1 1
## Manaphy Mandibuzz Manectric Mankey
## 1 1 1 1
## Mantine Mantyke Maractus Mareanie
## 1 1 1 1
## Mareep Marill Marowak Marshadow
## 1 1 1 1
## Marshtomp Masquerain Mawile Medicham
## 1 1 1 1
## Meditite Mega Abomasnow Mega Absol Mega Aegislash
## 1 1 1 1
## Mega Aerodactyl Mega Aggron Mega Alakazam Mega Altaria
## 1 1 1 1
## Mega Ampharos Mega Articuno Mega Audino Mega Banette
## 1 1 1 1
## Mega Basculin Mega Beedrill Mega Blastoise Mega Blaziken
## 1 1 1 1
## Mega Calyrex Mega Calyrex X Mega Camerupt Mega Castform
## 1 1 1 1
## Mega Castform X Mega Charizard Mega Charizard X Mega Corsola
## 2 1 1 1
## Mega Darmanitan Mega Darmanitan X Mega Darumaka Mega Deoxys
## 1 2 1 1
## Mega Deoxys X Mega Diancie Mega Diglett Mega Dugtrio
## 2 1 1 1
## Mega Eevee Mega Eiscue Mega Eternatus Mega Exeggutor
## 1 1 1 1
## Mega Farfetch'd Mega Gallade Mega Garchomp Mega Gardevoir
## 1 1 1 1
## Mega Gengar Mega Geodude Mega Giratina Mega Glalie
## 1 1 1 1
## Mega Golem Mega Gourgeist Mega Gourgeist X Mega Graveler
## 1 1 2 1
## Mega Greninja Mega Grimer Mega Groudon Mega Gyarados
## 1 1 1 1
## Mega Heracross Mega Hoopa Mega Houndoom Mega Indeedee
## 1 1 1 1
## Mega Kangaskhan Mega Keldeo Mega Kyogre Mega Kyurem
## 1 1 1 1
## Mega Kyurem X Mega Landorus Mega Latias Mega Latios
## 1 1 1 1
## Mega Linoone Mega Lopunny Mega Lucario Mega Lycanroc
## 1 1 1 1
## Mega Lycanroc X Mega Manectric Mega Marowak Mega Mawile
## 1 1 1 1
## Mega Medicham Mega Meloetta Mega Meowstic Mega Meowth
## 1 1 1 1
## Mega Meowth X Mega Metagross Mega Mewtwo Mega Mewtwo X
## 1 1 1 1
## Mega Minior Mega Moltres Mega Morpeko Mega Mr. Mime
## 1 1 1 1
## Mega Muk Mega Necrozma Mega Necrozma X Mega Ninetales
## 1 1 2 1
## Mega Oricorio Mega Oricorio X Mega Persian Mega Pidgeot
## 1 2 1 1
## Mega Pikachu Mega Pinsir Mega Ponyta Mega Pumpkaboo
## 1 1 1 1
## Mega Pumpkaboo X Mega Raichu Mega Rapidash Mega Raticate
## 2 1 1 1
## Mega Rattata Mega Rayquaza Mega Rockruff Mega Rotom
## 1 1 1 1
## Mega Rotom X Mega Sableye Mega Salamence Mega Sandshrew
## 4 1 1 1
## Mega Sandslash Mega Sceptile Mega Scizor Mega Sharpedo
## 1 1 1 1
## Mega Shaymin Mega Slowbro Mega Slowbro X Mega Slowking
## 1 1 1 1
## Mega Slowpoke Mega Steelix Mega Stunfisk Mega Swampert
## 1 1 1 1
## Mega Thundurus Mega Tornadus Mega Toxtricity Mega Tyranitar
## 1 1 1 1
## Mega Urshifu Mega Venusaur Mega Vulpix Mega Weezing
## 1 1 1 1
## Mega Wishiwashi Mega Wormadam Mega Wormadam X Mega Yamask
## 1 1 1 1
## Mega Zacian Mega Zamazenta Mega Zapdos Mega Zigzagoon
## 1 1 1 1
## Mega Zygarde Mega Zygarde X Meganium Melmetal
## 1 1 1 1
## Meloetta Meltan Meowstic Meowth
## 1 1 1 1
## Mesprit Metagross Metang Metapod
## 1 1 1 1
## Mew Mewtwo Mienfoo Mienshao
## 1 1 1 1
## Mightyena Milcery Milotic Miltank
## 1 1 1 1
## Mime Jr. Mimikyu Minccino Minior
## 1 1 1 1
## Minun Misdreavus Mismagius Moltres
## 1 1 1 1
## Monferno Morelull Morgrem Morpeko
## 1 1 1 1
## Mothim Mr. Mime Mr. Rime Mudbray
## 1 1 1 1
## Mudkip Mudsdale Muk Munchlax
## 1 1 1 1
## Munna Murkrow Musharna Naganadel
## 1 1 1 1
## Natu Necrozma Nickit Nidoking
## 1 1 1 1
## Nidoqueen Nidoranâ\231‚ Nidoranâ\231\200 Nidorina
## 1 1 1 1
## Nidorino Nihilego Nincada Ninetales
## 1 1 1 1
## Ninjask Noctowl Noibat Noivern
## 1 1 1 1
## Nosepass Numel Nuzleaf Obstagoon
## 1 1 1 1
## Octillery Oddish Omanyte Omastar
## 1 1 1 1
## Onix Oranguru Orbeetle Oricorio
## 1 1 1 1
## Oshawott Pachirisu Palkia Palossand
## 1 1 1 1
## Palpitoad Pancham Pangoro Panpour
## 1 1 1 1
## Pansage Pansear Paras Parasect
## 1 1 1 1
## Passimian Patrat Pawniard Pelipper
## 1 1 1 1
## Perrserker Persian Petilil Phanpy
## 1 1 1 1
## Phantump Pheromosa Phione Pichu
## 1 1 1 1
## Pidgeot Pidgeotto Pidgey Pidove
## 1 1 1 1
## Pignite Pikachu Pikipek Piloswine
## 1 1 1 1
## Pincurchin Pineco Pinsir Piplup
## 1 1 1 1
## Plusle Poipole Politoed Poliwag
## 1 1 1 1
## Poliwhirl Poliwrath Polteageist Ponyta
## 1 1 1 1
## Poochyena Popplio Porygon Porygon-Z
## 1 1 1 1
## Porygon2 Primarina Primeape Prinplup
## 1 1 1 1
## Probopass Psyduck Pumpkaboo Pupitar
## 1 1 1 1
## Purrloin Purugly Pyroar Pyukumuku
## 1 1 1 1
## Quagsire Quilava Quilladin Qwilfish
## 1 1 1 1
## Raboot Raichu Raikou Ralts
## 1 1 1 1
## Rampardos Rapidash Raticate Rattata
## 1 1 1 1
## Rayquaza Regice Regidrago Regieleki
## 1 1 1 1
## Regigigas Regirock Registeel Relicanth
## 1 1 1 1
## Remoraid Reshiram Reuniclus Rhydon
## 1 1 1 1
## Rhyhorn Rhyperior Ribombee Rillaboom
## 1 1 1 1
## Riolu Rockruff Roggenrola Rolycoly
## 1 1 1 1
## Rookidee Roselia Roserade Rotom
## 1 1 1 1
## Rowlet Rufflet Runerigus Sableye
## 1 1 1 1
## Salamence Salandit Salazzle Samurott
## 1 1 1 1
## Sandaconda Sandile Sandshrew Sandslash
## 1 1 1 1
## Sandygast Sawk Sawsbuck Scatterbug
## 1 1 1 1
## Sceptile Scizor Scolipede Scorbunny
## 1 1 1 1
## Scrafty Scraggy Scyther Seadra
## 1 1 1 1
## Seaking Sealeo Seedot Seel
## 1 1 1 1
## Seismitoad Sentret Serperior Servine
## 1 1 1 1
## Seviper Sewaddle Sharpedo Shaymin
## 1 1 1 1
## Shedinja Shelgon Shellder Shellos
## 1 1 1 1
## Shelmet Shieldon Shiftry Shiinotic
## 1 1 1 1
## Shinx Shroomish Shuckle Shuppet
## 1 1 1 1
## Sigilyph Silcoon Silicobra Silvally
## 1 1 1 1
## Simipour Simisage Simisear Sinistea
## 1 1 1 1
## Sirfetch'd Sizzlipede Skarmory Skiddo
## 1 1 1 1
## Skiploom Skitty Skorupi Skrelp
## 1 1 1 1
## Skuntank Skwovet Slaking Slakoth
## 1 1 1 1
## Sliggoo Slowbro Slowking Slowpoke
## 1 1 1 1
## Slugma Slurpuff Smeargle Smoochum
## 1 1 1 1
## Sneasel Snivy Snom Snorlax
## 1 1 1 1
## Snorunt Snover Snubbull Sobble
## 1 1 1 1
## Solgaleo Solosis Solrock Spearow
## 1 1 1 1
## Spectrier Spewpa Spheal Spinarak
## 1 1 1 1
## Spinda Spiritomb Spoink Spritzee
## 1 1 1 1
## Squirtle Stakataka Stantler Staraptor
## 1 1 1 1
## Staravia Starly Starmie Staryu
## 1 1 1 1
## Steelix Steenee Stonjourner Stoutland
## 1 1 1 1
## Stufful Stunfisk Stunky Sudowoodo
## 1 1 1 1
## Suicune Sunflora Sunkern Surskit
## 1 1 1 1
## Swablu Swadloon Swalot Swampert
## 1 1 1 1
## Swanna Swellow Swinub Swirlix
## 1 1 1 1
## Swoobat Sylveon Taillow Talonflame
## 1 1 1 1
## Tangela Tangrowth Tapu Bulu Tapu Fini
## 1 1 1 1
## Tapu Koko Tapu Lele Tauros Teddiursa
## 1 1 1 1
## Tentacool Tentacruel Tepig Terrakion
## 1 1 1 1
## Thievul Throh Thundurus Thwackey
## 1 1 1 1
## Timburr Tirtouga Togedemaru Togekiss
## 1 1 1 1
## Togepi Togetic Torchic Torkoal
## 1 1 1 1
## Tornadus Torracat Torterra Totodile
## 1 1 1 1
## Toucannon Toxapex Toxel Toxicroak
## 1 1 1 1
## Toxtricity Tranquill Trapinch Treecko
## 1 1 1 1
## Trevenant Tropius Trubbish Trumbeak
## 1 1 1 1
## Tsareena Turtonator Turtwig Tympole
## 1 1 1 1
## Tynamo Type: Null Typhlosion Tyranitar
## 1 1 1 1
## Tyrantrum Tyrogue Tyrunt Umbreon
## 1 1 1 1
## Unfezant Unown Ursaring Urshifu
## 1 1 1 1
## Uxie Vanillish Vanillite Vanilluxe
## 1 1 1 1
## Vaporeon Venipede Venomoth Venonat
## 1 1 1 1
## Venusaur Vespiquen Vibrava Victini
## 1 1 1 1
## Victreebel Vigoroth Vikavolt Vileplume
## 1 1 1 1
## Virizion Vivillon Volbeat Volcanion
## 1 1 1 1
## Volcarona Voltorb Vullaby Vulpix
## 1 1 1 1
## Wailmer Wailord Walrein Wartortle
## 1 1 1 1
## Watchog Weavile Weedle Weepinbell
## 1 1 1 1
## Weezing Whimsicott Whirlipede Whiscash
## 1 1 1 1
## Whismur Wigglytuff Wimpod Wingull
## 1 1 1 1
## Wishiwashi Wobbuffet Woobat Wooloo
## 1 1 1 1
## Wooper Wormadam Wurmple Wynaut
## 1 1 1 1
## Xatu Xerneas Xurkitree Yamask
## 1 1 1 1
## Yamper Yanma Yanmega Yungoos
## 1 1 1 1
## Yveltal Zacian Zamazenta Zangoose
## 1 1 1 1
## Zapdos Zarude Zebstrika Zekrom
## 1 1 1 1
## Zeraora Zigzagoon Zoroark Zorua
## 1 1 1 1
## Zubat Zweilous Zygarde
## 1 1 1
##
## $Total
## NULL
##
## $HP
## NULL
##
## $Attack
## NULL
##
## $Defence
## NULL
##
## $Sp_attack
## NULLd.df=data.frame(d2$HP,d2$Attack)
head(d.df)Summarize using a grouping variable:
head(aggregate(d2[2:5], by = list(d2$Attack), FUN = sum))head(aggregate(d2$Attack ~ d2$Defence, data = d2, FUN = mean))head(aggregate(cbind(d2$Attack, d2$Defence) ~ d2$Sp_attack, data = d2, FUN = mean))head(aggregate(. ~ d2$Defence, data = d2, FUN = mean))## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NApw.agg = aggregate(d2$Attack ~ d2$Defence * d2$Sp_attack * d2$HP, data = d2, FUN = mean)
head(pw.agg)head(aggregate(d2$Attack ~ . , data = d2, FUN = mean))head(aggregate(d2$HP ~ 1 , data = d2, FUN = mean))Curvilinear regression:
lm(d2$HP ~ d2$Attack, data = d2)##
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
##
## Coefficients:
## (Intercept) d2$Attack
## 40.6616 0.3654fw.lm = lm(d2$HP ~ d2$Attack, data = d2)
summary(fw.lm)##
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.552 -14.033 -3.070 8.757 210.684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.66163 1.98019 20.53 <2e-16 ***
## d2$Attack 0.36545 0.02283 16.01 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.91 on 1043 degrees of freedom
## Multiple R-squared: 0.1972, Adjusted R-squared: 0.1965
## F-statistic: 256.3 on 1 and 1043 DF, p-value: < 2.2e-16fw.lm$rank## [1] 2coef(fw.lm)## (Intercept) d2$Attack
## 40.6616348 0.3654456 confint(fw.lm)## 2.5 % 97.5 %
## (Intercept) 36.7760168 44.5472528
## d2$Attack 0.3206517 0.4102395 confint(fw.lm, parm = c('(Intercept)', 'HP'), level = 0.9)## 5 % 95 %
## (Intercept) 37.40161 43.92166
## HP NA NAhead(fitted(fw.lm))## 1 2 3 4 5 6
## 58.56847 63.31926 70.62818 77.20620 59.66481 64.05015head(residuals(fw.lm))## 1 2 3 4 5 6
## -13.568470 -3.319263 9.371824 2.793803 -20.664807 -6.050155formula(fw.lm)## d2$HP ~ d2$Attackfw.lm$cal## lm(formula = d2$HP ~ d2$Attack, data = d2)plot(d2$Attack, d2$HP)
plot(~ d2$Attack + d2$Defence, data = d2)
plot(d2$Attack ~ d2$Sp_defence, data = d2)
abline(lm(d2$HP ~ d2$Attack, data = d2))
abline(a = coef(fw.lm[1], b = coef(fw.lm[2])))
abline(coef(fw.lm))
anova(fw.lm)mf.lm = lm(d2$HP ~ d2$Attack, data = d2)
mf.lm##
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
##
## Coefficients:
## (Intercept) d2$Attack
## 40.6616 0.3654pb.lm = lm(d2$HP ~ d2$Attack, data = d2)
pb.lm##
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
##
## Coefficients:
## (Intercept) d2$Attack
## 40.6616 0.3654mf.lm = lm(d2$HP ~ 1, data = d2)
pb.lm = lm(d2$HP ~ 1, data = d2)
mf.lm##
## Call:
## lm(formula = d2$HP ~ 1, data = d2)
##
## Coefficients:
## (Intercept)
## 70.07pb.lm##
## Call:
## lm(formula = d2$HP ~ 1, data = d2)
##
## Coefficients:
## (Intercept)
## 70.07add1(mf.lm, scope = d2)mf.lm = lm(d2$HP ~ d2$Attack, data = d2)
mf.lm##
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
##
## Coefficients:
## (Intercept) d2$Attack
## 40.6616 0.3654mf.lm = lm(d2$HP ~ d2$Attack + d2$Defence, data = d2)
summary(mf.lm)##
## Call:
## lm(formula = d2$HP ~ d2$Attack + d2$Defence, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -69.002 -13.623 -2.984 8.762 214.203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.55648 2.22273 16.447 < 2e-16 ***
## d2$Attack 0.31922 0.02550 12.521 < 2e-16 ***
## d2$Defence 0.10480 0.02646 3.962 7.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.74 on 1042 degrees of freedom
## Multiple R-squared: 0.2092, Adjusted R-squared: 0.2076
## F-statistic: 137.8 on 2 and 1042 DF, p-value: < 2.2e-16mf.lm = lm(d2$HP ~ d2$Attack, data = d2)
mf.lm##
## Call:
## lm(formula = d2$HP ~ d2$Attack, data = d2)
##
## Coefficients:
## (Intercept) d2$Attack
## 40.6616 0.3654mf.lm1 = lm(d2$Attack ~ d2$HP, data = d2)
mf.lm2 = lm(d2$Attack ~ ., data = d2)
anova(mf.lm1,mf.lm2)pg.lm = lm(d2$HP ~ log(d2$Attack), data = d2)
summary(pg.lm)##
## Call:
## lm(formula = d2$HP ~ log(d2$Attack), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -73.750 -14.750 -3.439 8.813 241.564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -28.490 7.158 -3.98 7.36e-05 ***
## log(d2$Attack) 22.943 1.657 13.85 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.52 on 1043 degrees of freedom
## Multiple R-squared: 0.1553, Adjusted R-squared: 0.1545
## F-statistic: 191.7 on 1 and 1043 DF, p-value: < 2.2e-16 bbel.lm = lm(d2$HP ~ d2$Attack + I(Attack^2), data = d2)
bbel.lm##
## Call:
## lm(formula = d2$HP ~ d2$Attack + I(Attack^2), data = d2)
##
## Coefficients:
## (Intercept) d2$Attack I(Attack^2)
## 33.823443 0.545988 -0.001022plot(d2$Attack ~ d2$Defence, data = d2)
plot(d2$Attack ~ d2$Sp_attack, data = d2)
lines(d2$Attack, fitted(pg.lm))
lines(d2$Defence, fitted(bbel.lm))
head(predict(mf.lm))## 1 2 3 4 5 6
## 58.56847 63.31926 70.62818 77.20620 59.66481 64.05015prd = predict(mf.lm, interval = 'confidence', level = 0.95)
attach(d2)## The following objects are masked from head(d2):
##
## Attack, Defence, HP, Name, Sp_attack, Sp_defence, Speed, Totalprd = data.frame(prd, d2)
detach(d2)
prd = data.frame(prd)
prd$d2 = d2$d2
head(prd)plot(d2$HP ~ d2$Attack, data =d2) # basic plot
lines(prd$HP, prd$fit) # also best fit
lines(prd$Attack, prd$lwr, lty = 2) # lower CI
lines(prd$Defence, prd$upr, lty = 2) # upper CI
mf.lm = lm(d2$Attack ~ d2$Defence + d2$Sp_attack, data = d2)
plot(mf.lm)
bf.m = apply(d2, 2, mean, na.rm = TRUE)## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NAbf.sd = apply(d2, 2, sd, na.rm = TRUE)## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercionBar Graphs Using ggplot() with geom_col()
library(gcookbook)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
ggplot(d2, aes(x = d2$Defence, y = d2$Sp_attack)) + geom_col()## Warning: Use of `d2$Defence` is discouraged. Use `Defence` instead.## Warning: Use of `d2$Sp_attack` is discouraged. Use `Sp_attack` instead.
ggplot(d2, aes(x = d2$HP, y = d2$Attack)) + geom_col(fill = "yellow", colour = "black")## Warning: Use of `d2$HP` is discouraged. Use `HP` instead.## Warning: Use of `d2$Attack` is discouraged. Use `Attack` instead.
ggplot(d2, aes(x = d2$HP, y =d2$Attack, fill = HP)) + geom_col(position = "dodge")## Warning: Use of `d2$HP` is discouraged. Use `HP` instead.
## Warning: Use of `d2$Attack` is discouraged. Use `Attack` instead.
Bar Graphs Using ggplot() with geom_bar()
ggplot(d2, aes(x = Attack)) + geom_bar()
Using Colors in a Bar Graph:
a2 <- d2 %>% arrange(desc(Attack)) %>% slice(1:10)
a2Adjusting Bar Width and Spacing:
ggplot(d2, aes(x = Attack, y = Defence)) + geom_col(width = 0.5)
ggplot(d2, aes(x = Attack, y = HP)) + geom_col(width = 1)
ggplot(d2, aes(x = Attack, y = HP, fill = Attack)) + geom_col(width = 0.5, position = "dodge")
ggplot(d2, aes(x = Attack, y =HP, fill = Attack)) +geom_col(width = 0.5, position = position_dodge(0.7))
ggplot(d2, aes(x = Attack, y = HP, fill = Attack)) +geom_col() + guides(fill = guide_legend(reverse = TRUE))
ggplot(d2, aes(x = Attack, y = HP, fill = Attack)) + geom_col(position = position_stack(reverse = TRUE)) + guides(fill = guide_legend(reverse = TRUE))
ggplot(d2, aes(x = Attack, y = HP, fill = Attack)) + geom_col(position = "fill") + scale_y_continuous(labels = scales::percent)
# using group_by command:
a2 <- d2 %>%group_by(Attack) %>% mutate(Sp_attack = Attack / sum(Attack) * 100)
head(a2)ggplot(d2, aes(x = Attack, y =Sp_attack , fill = Attack)) + geom_col()
ggplot(d2, aes(x = interaction(Attack, HP), y = Defence)) +geom_col() + geom_text(aes(label = Defence), vjust = 1.5, colour = "white")